{
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  "name": ""
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Simulation Results for varying number of maximum iterations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook shows BER and Sum Capacity results for different IA\n",
      "algorithms when the maximum number of allowed iterations is limited.  Note\n",
      "that the algorithm might run less iterations than the allowed maximum if\n",
      "the precoders do not change significantly from one iteration to the next\n",
      "one.  The maximum number of allowed iterations vary from 5 to 60, except\n",
      "for the closed form algorithm, which is not iterative. The solid lines\n",
      "indicate the BER or Sum Capacity in the left axis, while the dashed lines\n",
      "indicate the mean number of iterations that algorithm used.\n",
      "\n",
      "Let's perform some initializations.\n",
      "\n",
      "First we enable the \"inline\" mode for plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we import some modules we use and add the PyPhysim to the python path."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append(\"/home/darlan/cvs_files/pyphysim\")\n",
      "# xxxxxxxxxx Import Statements xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "from util.simulations import SimulationRunner, SimulationParameters, SimulationResults, Result\n",
      "from comm import modulators, channels\n",
      "from util.conversion import dB2Linear\n",
      "from util import misc\n",
      "from ia import ia\n",
      "import numpy as np\n",
      "from pprint import pprint\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we set the transmit parameters and load the simulation results from the file corresponding to those transmit parameters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# xxxxx Parameters xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "#params = SimulationParameters.load_from_config_file('ia_config_file.txt')\n",
      "K = 3\n",
      "Nr = 2\n",
      "Nt = 6\n",
      "Ns = 2\n",
      "M = 4\n",
      "modulator = \"PSK\"\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxx Results base name xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "base_name = 'results_{M}-{modulator}_{Nr}x{Nt}_({Ns})_MaxIter_[120]'.format(M=M, modulator=modulator, Nr=Nr, Nt=Nt, Ns=Ns)\n",
      "base_name_no_iter = 'results_{M}-{modulator}_{Nr}x{Nt}_({Ns})_MaxIter_[120]'.format(M=M, modulator=modulator, Nr=Nr, Nt=Nt, Ns=Ns)  # Used only for the closed form algorithm, which is not iterative\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "alt_min_results = SimulationResults.load_from_file(\n",
      "    'ia_alt_min_{0}.pickle'.format(base_name))\n",
      "closed_form_results = SimulationResults.load_from_file(\n",
      "    'ia_closed_form_{0}.pickle'.format(base_name_no_iter))\n",
      "# closed_form_first_results = SimulationResults.load_from_file(\n",
      "#     'ia_closed_form_first_init_{0}.pickle'.format(base_name))\n",
      "# max_sinrn_results = SimulationResults.load_from_file(\n",
      "#     'ia_max_sinr_{0}.pickle'.format(base_name))\n",
      "# min_leakage_results = SimulationResults.load_from_file(\n",
      "#     'ia_min_leakage_{0}.pickle'.format(base_name))\n",
      "mmse_results = SimulationResults.load_from_file(\n",
      "    'ia_mmse_{0}.pickle'.format(base_name))\n",
      "# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's define helper methods to get mean number of IA iterations from a simulation results object."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Helper function to get the number of repetitions for a given set of transmit parameters\n",
      "def get_num_runned_reps(sim_results_object, fixed_params=dict()):\n",
      "    all_runned_reps = np.array(sim_results_object.runned_reps)\n",
      "    indexes = sim_results_object.params.get_pack_indexes(fixed_params)\n",
      "    return all_runned_reps[indexes]\n",
      "\n",
      "# Helper function to get the number of IA runned iterations for a given set of transmit parameters\n",
      "def get_num_mean_ia_iterations(sim_results_object, fixed_params=dict()):\n",
      "    return sim_results_object.get_result_values_list('ia_runned_iterations', fixed_params)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the SNR values from the simulation parameters object."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SNR_alt_min = np.array(alt_min_results.params['SNR'])\n",
      "SNR_closed_form = np.array(closed_form_results.params['SNR'])\n",
      "# SNR_max_SINR = np.array(max_sinrn_results.params['SNR'])\n",
      "# SNR_min_leakage = np.array(min_leakage_results.params['SNR'])\n",
      "SNR_mmse = np.array(mmse_results.params['SNR'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a function that we can call to plot the BER.\n",
      "This function will plot the BER for all SNR values for the four IA algorithms, given the desired \"max_iterations\" parameter value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_ber(max_iterations, ax=None):\n",
      "    ber_alt_min = alt_min_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_alt_min = np.abs([i[1] - i[0] for i in ber_CF_alt_min])\n",
      "\n",
      "    ber_closed_form = closed_form_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_closed_form = closed_form_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_closed_form = np.abs([i[1] - i[0] for i in ber_CF_closed_form])\n",
      "\n",
      "    # ber_closed_form_first = closed_form_first_results.get_result_values_list('ber')\n",
      "    # ber_CF_closed_form_first = closed_form_first_results.get_result_values_confidence_intervals('ber', P=95)\n",
      "    # ber_errors_closed_form_first = np.abs([i[1] - i[0] for i in ber_CF_closed_form_first])\n",
      "\n",
      "    # ber_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "    #     'ber',\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # ber_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "    #     'ber',\n",
      "    #     P=95,\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # ber_errors_max_sinr = np.abs([i[1] - i[0] for i in ber_CF_max_sinr])\n",
      "\n",
      "    # ber_min_leakage = min_leakage_results.get_result_values_list('ber')\n",
      "    # ber_CF_min_leakage = min_leakage_results.get_result_values_confidence_intervals('ber', P=95)\n",
      "    # ber_errors_min_leakage = np.abs([i[1] - i[0] for i in ber_CF_min_leakage])\n",
      "\n",
      "    ber_mmse = mmse_results.get_result_values_list(\n",
      "        'ber',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'ber',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    ber_errors_mmse = np.abs([i[1] - i[0] for i in ber_CF_mmse])\n",
      "\n",
      "    if ax is None:\n",
      "        fig, ax = plt.subplots(nrows=1, ncols=1)\n",
      "    ax.errorbar(SNR_alt_min, ber_alt_min, ber_errors_alt_min, fmt='-r*', elinewidth=2.0, label='Alt. Min.')\n",
      "    ax.errorbar(SNR_closed_form, ber_closed_form, ber_errors_closed_form, fmt='-b*', elinewidth=2.0, label='Closed Form')\n",
      "    # ax.errorbar(SNR_max_SINR, ber_max_sinr, ber_errors_max_sinr, fmt='-g*', elinewidth=2.0, label='Max SINR')\n",
      "    # ax.errorbar(SNR, ber_min_leakage, ber_errors_min_leakage, fmt='-k*', elinewidth=2.0, label='Min Leakage.')\n",
      "    ax.errorbar(SNR_mmse, ber_mmse, ber_errors_mmse, fmt='-m*', elinewidth=2.0, label='MMSE.')\n",
      "\n",
      "    ax.set_xlabel('SNR')\n",
      "    ax.set_ylabel('BER')\n",
      "    title = 'BER for Different Algorithms ({max_iterations} Max Iterations)\\nK={K}, Nr={Nr}, Nt={Nt}, Ns={Ns}, {M}-{modulator}'.replace(\"{max_iterations}\", str(max_iterations))\n",
      "    ax.set_title(title.format(**alt_min_results.params.parameters))\n",
      "\n",
      "    ax.set_yscale('log')\n",
      "    leg = ax.legend(fancybox=True, shadow=True, loc='lower left', bbox_to_anchor=(0.01, 0.01), ncol=4)\n",
      "    ax.grid(True, which='both', axis='both')\n",
      "    \n",
      "    # Lets plot the mean number of ia iterations\n",
      "    ax2 = ax.twinx()\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    # mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations})\n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations})\n",
      "    ax2.plot(SNR_alt_min, mean_alt_min_ia_terations, '--r*')\n",
      "    # ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations, '--g*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations, '--m*')\n",
      "    \n",
      "    # Horizontal line with the max alowed ia iterations\n",
      "    ax2.hlines(max_iterations, SNR_alt_min[0], SNR_alt_min[-1], linestyles='dashed')\n",
      "    ax2.set_ylim(0, max_iterations*1.1)\n",
      "    ax2.set_ylabel('IA Mean Iterations')\n",
      "\n",
      "    # Set the X axis limits\n",
      "    ax.set_xlim(SNR_alt_min[0], SNR_alt_min[-1])\n",
      "    # Set the Y axis limits\n",
      "    ax.set_ylim(1e-6, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plot the BER"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can create a 4x4 grids if plots and call the plot_ber function to plot in each subplot."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(1,1,figsize=(10,7.5))\n",
      "plot_ber(120, ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zAoPBl6iortStu5Vy5Q4xduxgxowZhr29cTl7+7I1QppDKcXQoeNZvvwrGSm9\ng8zjyU9qYp7UxTypi3lSl/y03gha5S3memavYMbQNaR+2Zhhjz8OVasaH9WqQdWq2FStilOVKjg5\n2Vo0z6ysh28sb92C2Fjzr508OZRr16qQlvYnoOPCBQPlyk3kv/8N4oMPICPDuGxmJqamMOdxZ1zY\n88Xxmk0xz1BduXITq1ffZNWqzTJSKoQQQphhlY3gqYxYRlepwlAXF/THjkFCAgHlysGNG+gvXDDG\nKSlQqRJ6Z2eoVImABg2galX0t24Z43btjPHFi8a4d2+oUAH99u1A0dxr0M4OwsMLX97J6UG3p+Pf\n/z7C3LmnefTRdKKiDLzyymG6dHHMs7zBAB06BJCeDn/8oSczE1q2NMa7dhnjZs2McXi4MW7Y0Bgf\nOaInIwOqVQsgIwNOnjS+Xr268fXISGPs5maMr1wxxk5Oxjg2Vk9WFtjYGOOUFOPrmZnG+tja6ilX\nDlxcAnBwgOxsY1y5sjG+dcsY16pljOPijLGnpzGOidFjbw+NGxvjCxf0HDu2hWPHDmEw+JKaOoFx\n475h6tTP6d9/MJ6e7tjZQdu2AZQrBwcP6rGzg06djPFffxnjwEBjvGuXHhsby9+rsihjpRSbNu2n\nS5cubC/C/d2a4y5durBx418opdDpdBbPR2txDq3ko4U4ICBAU/loKc6hlXwsHWudVR4aHuDoxnOh\nIQQNGFDwgtnZEBcHN27A9evGfwv7OjPz9uhirhHGfF/nxFWqGIe2LGjOnBAaNapD//7dWbVqM6dP\nR/HGG2MtmtO9UMpY7pxRS3OPgl4r7D3h4YoTJzaSnPwnBsMcdLrpODl1oWbNIGrW1P3TiN77w8bG\neAJScTzs7Ipv3eYetrawcqXMKb2TzLM1T05EM0/qYp7UJT+lFDY2Npo+NGyVjeCzVYZwgmNM+u8U\ngscFF93KU1Ph5s17bx5v3AAnp8Ibxtxfu7oW+TFRpRTjhw7lq+XL5ZvvHzm/2CtXjiY2tjZLlvR8\noF/wShn/prjf5vFBHllZxbv+9PRQYAXgC3QFtgKHsLcfTPnyw7C1Ne6aNjaYvjb3XGGvW+q5B3mP\nXh/Kxo0ryM72JSamKzVrbsXO7hC9eg2mZ89h6HTG+bU2Npi+LorntLhOc8+vXLmRkSMXs2zZc9Ig\n5/LLLxsZMULqciepS36//LKRQYN6SiNYlHQ6Hc/qnsVX50sbmzY4N3bGqYETTvX/efzztUNdB2zs\nirbhykfMsJUJAAAgAElEQVQpSEi4t4YxJ05KgsqV7z7ieOfXzs53TWPjL7+weMQInlu27O6jpGVI\nzkhp5cr2xMZmWM1IaXEKC1N8+eVGNm/+k8TEICpW3MTjj3dh9OggOnTQYTAYT7TKzs77b2HPPch7\nivq5B13PsWOKs2eNo8dKBaHTbaJ8+S7UqRNEvXo6lML0MBjIEz/sc1pep8Fg/o8GGIxONwy4fQJa\n7r897/ZcSb2nOLedkBBKWprxBD3oik63FZ3uEE5Og3F1HZbv/bkVxfPFue6H2eb166EkJNyui42N\ncX+pVGkwjzwyzPwKSrlr13LX5D1pBIuSTqfj6QpPM2LJCIK6B5F2Lo3Us6mknkk1/vvP1xlXMnDw\ncMjXIDrWd8SpnhO2zhY6kSQz8/ao4700j9evG/9MN9MkhkZGsuKvv/DV6fjPlSvMrF+fQw4ODH7p\nJYaNG2eZzyc0LWek1MNDR1SU4YFHSksTqUl+ef9omEPFitPp3r0L48cHERhobJABcv/2uNtzhb1e\nVO8p7m3v3Kn47ruN7NjxJ0lJc6hQYTqdO3dhxAjjH1MF/TYtiueLc90Pu829exU//riRXbv+JDl5\nDi4u0+nYsQtDhgTRtm3ZPEq1b1/umryv6UbQKk8WGbFkBBdOX8BugB0uvi64+LrkW8aQYSAtMi1P\ngxj3RxxpZ9NIi0zDrrJd/gbxn6/LuZUrvuTLlYMaNYyPe6GU8dRhMw3j0KNHqZKWxp8JCegAw9mz\nTLSxIWjOHNizBxo1uv1o0KDQkUVR+p0+HcWSJT3yzCkt66Qm+QUG6rh5U8emTWl4eU0lKsrA4ME6\nnniibP5Sz/H00zpsbHTs2HG7LqNG6RgwoGzXpXFjHeXL69i9+3ZdnnuubNelSZPbNdE6qxwRfNiU\nVbYi/VJ6nhHEtLO3RxZ15XR5RhId6zuavravYa+peXgbf/mFTaNHE125MrVjY+k5bx5BjRrBqVN5\nH+fOGUcTczeHDRsa//X0NDaopZBermllltQlP6lJXjK9wjypi3lSl/xyajJwYI98fcu+fft44403\nCAsLIyIigpdeeglbW1scHBz4/vvveeSRRwgJCWHRokXY2dkxc+ZMevXqVSx5lslG8G6UUmTeyDTb\nIKaeTSU7ORunevkbRKf6TjjUKYF5iXcImTOHOo0aYV+5MhmxsUSdPs3YN97Iv2B2Nly8mL9BPHUK\nrlyBunXzNok5j1q1rPpq1PLL3TypS35SE/OkLuZJXcyTuuR3Z9/ywQcfEBoaiouLC7t37yYgIIDP\nPvsMHx8fFi1axMmTJ3nttdfo1q0bBw4cIDU1lU6dOhEeHo69vX3R5yeN4P3JSsoi9ew/DeKd8xJj\nMnCs45ivQXSq74RjPUdsLXyB6wKlpcHZs+abxJSU2yOHdz7c3CyduRBCCKFpd/Ytq1atwsfHh+HD\nh7Nnzx5iYmKo8c90sQULFnDlyhXatm3L+vXrWbhwIQD9+/fnzTffpFWrVkWen1XOEQwODiY4OJgA\nC1zQc+eBncZ4QP7XDekGNv+0mfRL6bQs35LUs6nG+HI6za41o1zVchytchSH2g506dQFp/pOhMeH\nY1/bnq69u5ZI/neNmzUzxm3b3n597Vq4dImAChXg1Cn0S5dCdDQBV66AgwP66tXB3Z2ALl2gUSP0\nCQlQuzYBQUGW/zwSSyyxxBJLbMFYKYULec9j6N+/P5GRkaY4pwncvXs3CxYsYMeOHWzcuJFKlSqZ\nlqlQoQIJCQkUC2VlALOPWbNmmV1+1qxZmljekGVQqZGp6rURr5ldfpTjKBXeJlwdHXJUnZt5Tl1Z\nekXF7YhTM6bOKHT9BoNBjR4yWhkMBst93g4dlOrbV6mmTZVycFCqTh2lunZVs1q1smj9w8LCinX9\n1rr8yJEjNZWPLC/LW9vyYWFhmspHK8vn/pmrhXwsvbwttqoNbfIte/78edWuXTtTvGLFCuXj46PO\nnz+vlFLqt99+Uy+++KLp9X79+qkDBw6Y3e7DkkPDGqCUIvN6Zp65iLkPPWff+mdeYu6TV3LmJXo4\nsH71ej4Z8QmvLHuFXgOKZzLpfcnKKng+YkwMPPqo+UPNNWoU+XxEvV5v+utM3CZ1yU9qYp7UxTyp\ni3lSF+Pv9G/mfkPol6F43vJk5M2RBKrAPMtERkYyZMgQ9uzZQ2hoKIsWLWLNmjW4/TPl6urVq3Tr\n1o39+/eTlpZGu3btOHTokMwRhNLZCBYmKzHLNBcxd4O4MnwlW5O2Up/6jGEMS8ov4bz9eYYED+HF\nT160dNrmpaYWPB8xNdV8g9iwofGOLEIIIYTGZd7MZF/TfUTUjOCv838xOmk0ASogzzKRkZE8++yz\n7Ny5k2rVqlG3bl3ToeCAgABmzZrF4sWLWbRoEQaDgRkzZtCvX79iyVcaQSsWGxbLr1/8in6znjHJ\nY1hcbjG+tr60r9Ceqr2q4vqEK25PuOFQ07L3Q75ncXFw+rT5JrF8efNNYv364OhodnVKKT6cPp1p\nc+Zo6pI/QgghrE9WYhbJh5NJOZRCckQyyYeT8dvuh62j+RNB1/6yltDRoZxNOst+tb+Es7130gha\nuZwdLb5yPJViKzHi2xEEegcSty2OuG1xxIfFY1/THrcn3HB7wg3XAFfsKlnZOUJKGQ8pm2sQz5+H\nmjXNNokb9+9ncXCw3HrPDDl8k5/UxDypi3lSF/NKa13+7vA3yYeSKe9dHhc/F9OjQqsKBV42bsGc\nBXg28qT3wN6a7lusrCMQd7pw+gLDlwzHubIzt2JvceH0BZwHOuPc2JnaL9ZGZSuSDiYRvy2eSwsu\ncXzYcZybOZsaw4odKhb414xm6HTGZq9mTejSJe9rWVlw4cLtxvDECUI/+ogV0dH4GgxMALYOG8bn\nY8YwePhwhn3+uUU+ghBCCO0xZBm4deIWyRHGkb6a42ri3CD/Xbi8fvLCoZYDOtt7P7o0YfqEoky1\n2MiIYBmTnZZN4p5E42jhtnhS/pdChbYVcOtqbAwrtKhwXzu6FqmwMDZ++SV/bt7MnMREpjs40KVC\nBYKSktC1bAkBAcZHhw7GQ85CCCHKlKhPorj6w1VuHbuFg4eDaYSv+vDqOLqbn270oLTet0gjWMZl\nJWQRvz3edCg543IGrl1ccevqhusTrjg3drbK+XU5t97TeXhgiIqi55IlBPXsabwHc1gY6PUQEQF+\nfnkbQ7kfsxBCWDWlFBmXM0iOSMbR05HyzfL/wZ+wKwFsoLx3eexcivfgqNb7FmkES4mimpeRHpNO\n/B/xxG01NoYqS92eX/iEa5H/pVRc7unWeykpeRvDQ4fA39/YFAYGQvv24ORkifSLXWmdx/MwpCbm\nSV3Mk7qYZ6m6JO5P5PrP140ncUQkgw5c/F1wn+JOlZ5VSjyf3LTet8gcQZGHQw0Hqj9bnerPVkcp\nRerZVOK3xXNz7U3OvHKGclXL3W4MA10p51bO0imb9dz06YDxh1KBJ4qULw9duxofYGwMd+0yNoUz\nZ8Lhw9Cixe3GsF27UtsYCiGE1mUlZZEVn4WjR/4BieykbOwq2+E+1R0XPxfsa9hb5dEsS5ARQXHP\nlEGRfCjZNL8wYVcCzo2dTZepqdSpknbvp/wgkpNvN4Z6PRw5AjlzDHMawwIuXSOEEOLBZSVkkbAz\nwTTClxyRTPrldGqNq0WDTxpYOr37ovW+xSobwZEjR1rsXsMS344NGQbWL1xP0t9JND7bmOSIZE41\nPIVLCxd6PteTCq0q8OfOPzWT70PHSUnoFy6EiAgCzp2D//0PfYMG4OdHwKhR0LYt+r17tZOvxBJL\nLLHWYwUBgflfTzqQxIHxB6A+NP1XU1z8XNh/ZT/Yaiz/e4gDAwOlESxKWu+sLUWv15t2PkvJSsoi\n4c8E04knaRfScO3sajoj2dmr5E88Kda6JCXBzp2g1xsfR49C69YQEGAcMWzbFhy0eTFvLewvWiM1\nMU/qYp7UJT+lFOOHjuer5V+Z/VmflZxFyuF/LsZ80DjKZ0gz0PpIawtkW3K03rfIHEFRZOwq2FGl\nVxWq9DJOzM24lkHcH8bDyNHzozGkGnB93HgY2a2rG451rPywaoUK0LOn8QGQmHi7MXz1VTh2DNq0\nud0Ytmmj2cZQCCEe1rqV64hdE8v6Vevz3fc+KymL3TV3U97rnwsy+7tQY1QNynvLJbwsTUYERYlJ\nPZ9qHC3cGkf8H/HYVbIzzS90DXTFvqq9pVMsWgkJeUcMT5zI2xi2bi2NoRDC6i39einLPl2Gx1UP\nRsaO5IcGP3DO/hzDXxpO8Lhg03IqW1n9dWofhNb7FmkEhUUogyLlfymmxjBhZwJO9Z1Ml6lxfcwV\n2/Kl6MQTMDaGO3bcbgxPnjQePs7dGNqXsmZYCFHqGbIMfN3la3b9vYuxaWP53v17+s/rT68BveTM\nXbTft9hYOgFRNHImpVoLnY0OFx8XPF72wGedDx1vdKTh5w2xrWDLxTkX2VV9Fwc7HyTynUgSdiVg\nyDQ80HY0VZdKlaB3b/joIwgPh6gomDwZ4uLgpZegShXo1g3++1/YvRsyMootFU3VRSOkJuZJXcyT\nuhgpg+LU+FNkJ2STVS6LBXUXkJKQgk6nkybQSsgcQaEJNuVsqNSxEpU6VsLzbU+yU7KJ3xFP/LZ4\nTk86TerZVCp1qmSaX1i+eXl0Nlb+Q8bVFfr0MT7A2BDmjBhOnAinTxsvah0YaBw1bNUKymnzuo1C\niLLp3BvnuHXsFmqQYnjzvPe9F9ZBDg0Lq5BxI4P4sHjTNQyzErLynHji9Gj+Cz0rpXhv+nu8OedN\n6/zLNDY276Hks2fzNoYtW0pjKISwqITdCTh7OVPOVX4WFUTrfYs0gsIqpV1IM12mJm5bHLbOtqb5\nhW6Pu2H/iD1rf1lL6OhQhi8Znu8MNqsUGwt//nm7MTx3znh/5NyNoZ0M8gshhJZovW+RRrCUKMvX\ntFJKcevYLdOJJz9t+YkwFUZDp4b4xvtyrOExzpXLfwab1bt5M29jGBmZtzFs0cJsY2i81tdQvlq+\n3DpHSotJWf4euhupi3lSF/OkLvlpvW+R4QNh9XQ6HeWblad8s/K4v+ROra218HjPgz/D/kSHjlvn\nbjGw3UD6NOhj6VSLVpUq0K+f8QFw48btxnDsWLhwATp2vN0Y+vuDnR2bVq7k5urVbF61quD7MAsh\nhCgTZERQlDpx+jh+/fJXNq7ZiF1FOzLjM2nv0p72FdpTd0Zdqg+vjq1zKbs0jTnXr+cZMQw9dYoV\nSuGbmcl/gJmVK3PI1pbBw4cz7OOPLZ2tEELjrv18DZ29jmpPVbN0KlZF632LXD5GlDpuAW6k+qcy\n8oeRLL+2nOAVwTi+7kjTZU25ue4mez33cv7t86THpFs61eJVrRoMGACffw5HjjD04kUmTJqEAdAB\nhpQUJvbvz9AZMyydqRBC426svcHpSadxqpf/xDxh3aQRLCXkmlZ5TZg+gV4DerF9+3Z6DejFi2+8\niGsXV7x/88Z/pz+Z1zPZ33Q/J8acIPl/yZZOt0Tojh9HFxVFmr09gypWJDU7G926dejq1DE2jL/+\nCumlvDm+C/keMk/qYl5ZqkvctjhOjjqJ9+/euPi43HXZslSX0kIaQVHmODdyptHCRrQ53QbHRx05\n3O0wh3ocInZzrKaH7x9aQABR/v70+OEHXly9mp4rVhA1YQJER8OTT8Jnn0GtWjBunPGyNYYHu4i3\nEKL0SNidwLHBx2j2SzMqtqlo6XREMdDkHME//viDH3/8kZCQkHyvaf1Yu7A+hnQDV3+4SvQn0QC4\nT3Wn+rPVsXEog38nXbwIP/4Iy5ZBcjIMHQrDhkHTppbOTAhRwgzpBv7y+ouGCxpSpUcVS6djtbTe\nt2iuETx79ixr1qzh4MGDLFu2LN/rWi+osF5KKeK2xBH1SRQph1KoNaEWtV+oTbkqZfBCqUrB4cMQ\nGgo//AA1ahgbwsGDoWZNS2cnhCghWYlZ2FWUC4w8DK33LZob8qhfvz5Tp061dBpWR+ZlmHc/ddHp\ndFTuXhnfjb74bPEh7Xwa+xrs49QLp7h16lbxJWkBhdZFpwNfX/jwQ+Mo4QcfwKFD4OUFQUG3RwxL\nEfkeMk/qYl5Zqcv9NoFlpS6lSYk0gvv27SMwMBAAg8HA+PHj6dChA4GBgZw9exaAt956iyFDhhAf\nH18SKQlxVy7NXWjyTRPanGhDuWrlONjpIEf+dYT47fGa/suuWNjawhNPwNKlcOkSjBoFP/8M7u7G\nQ8cbNkBWlqWzFEII8QCK/dDwBx98QGhoKC4uLuzevZtVq1axdu1avv32W/bt28ecOXNYvXp1vvcN\nHz5cDg0Lzci+lc3VZVeJ+iQKWxdbPF7xoNqgatiU09ygesm5ft3YEIaGGm93N3iw8fBxq1bGEUUh\nhFUxZBmwsSvDP9OKidb7lmL/H2/QoAGrVq0yFWHnzp306NEDgLZt2xIeHm72feaaQCEsxdbZllrj\natHmeBs8/+3JlcVX2FdvHxc/vEhmfKal07OMatVgwgTYswd27gQ3NxgyBJo0gXffNTaHQgirkBmb\nyd9t/ib5UOma8iEKV+wzQPv3709kZKQpTkpKomLF26eg29raYjAYsLG59540ODgYT09PAFxdXfHz\n8zPd2zBnfkJZi3Oe00o+Wonnz59fpPvH9j+3gwsE/BFA0t9JrHx9JUnvJNFjTA/cJ7uz78I+TX3+\nEt1fZs9G36ULHD9OwLFj0LYt+ho1oFs3AmbMgCpVNPP5zcV31sbS+WgljoiIYMqUKZrJRytxadpf\nOrXoxOEeh0lulEx4bDgBPPj6ZH8x//NWy0rkrOHIyEiGDBnCnj17eOWVV2jXrh2DBg0CwMPDg6io\nqHtel9aHWC1Fr9ebdj5xW0nUJS06jUtfXOLK4iu4BbrhPtWdSu0rFes2H1aJ7C+ZmbBpk/HQ8YYN\nxnseDxsGvXuDo2PxbvsByPeQeVIX80pLXbJvZXO452HKe5Wn4ZcN0T3ktI7SUpeipPW+pcQbwVWr\nVvH777+zZMkS9u7dy7vvvsu6devueV1aL6gou7KSs4hZEkP0vGjsq9vj/oo71fpVQ2cr8+VITIRV\nq4xN4d9/Q//+xqawc2ewsbF0dkKUSYZ0A0f6HsH+EXuaLG2CzkZ+VhUHrfctJXZxoJy/Mvr168eW\nLVvo2LEjAEuWLLnvdQUHBxMcHJxneN7SQ78SS2znYscZ7zOoEEX9+PpEfRLFL5N+odqAavSb0w+7\nCnaayrfE4+Bg9J6ecP06ARcuwJQp6C9dMh46fvNNaN5cW/lKLHEpj//84U/QQedvO6Oz0Vk8n9Ia\na53mLihdGK131pai1+tNO5+4zdJ1SdibQPQn0cT9EUfN0TWp/VJtHN0tf1jU0nUxOXIEli83PqpU\nMY4SDhkCtWuXeCqaqYnGSF3Mk7qYJ3XJT+t9i42lExCiNKvUrhLNfm5Gy/CWqExFuE84x4YeI+nv\nJEunpg3e3vD++3DhAsyfDydOGJ/r2tV43cLEREtnKIQQpZqMCApRgrISsrgccplLn13CsZ4jHlM9\nqNK7iszNyS0tDdauNc4nDAuDnj2NI4VBQVCuDN7uTwhh1bTet1hlIzhy5EiZIyixVcedO3bm+i/X\nWT17Ndkp2fSd0ZcaI2uw468dmshPM/GaNbB9OwF//QWnTqHv2NE4p/CFF0Anc5oklvi+4m/0UF9D\n+ZSRODAwUBrBoqT1ztpS9Hq9aecTt2m9LkopEnYkEPVxFIl7Eqk1rha1JtTCoYZDsW5X63Ux69w5\n+OEH432Os7ONo4RDh0LDhkWyequsSQmQuphnbXW5+NFFYr6NoVVEK2zsbYptO9ZWl5Kg9b6l+PYG\nIUShdDodrp1d8V7jjf9OfzJjM9nvtZ8To0+QfESu8J9HvXowc6ZxHuGKFRAfD489Bu3awRdfGG95\nJ4TI59JXl7j85WV8NvsUaxMorJOMCAqhMZk3M7n89WUufXGJ8s3L4/GKB27d3R76Qq+lUlYWbN1q\nnE+4di106mQcKfzXv8DZ2dLZCWFxMctiOP/mefy2++FUz8nS6ZRJWu9brLIRlDmCEpeF2JBuYPWs\n1Vz7+RotnFvgMdWDY+7HsLW31UR+mouTk9HPmQNbthBw+jT07Yve2xv8/Ah44gnL5yexxCUdv6uH\n+dB6Z2vKNy1v+XzKaCxzBIuY1jtrS9Hr9aadT9xWGuqilCJuWxzRH0eTHJFMrRdrUeuFWthXtX/g\ndZaGutxVTIzx8HFoKFy5Yrw24bBh4OsLBYyslvqaPCCpi3nWUJekg8bLVFXwr1Bi27SGupQ0rfct\nNpZOQAhxdzqdjspdK+OzwQffrb6kXUjjr4Z/cXL8SW6dvGXp9LSpRg2YMgXCw2HLFnBwgKeeun3d\nwjvub66U4sdFizT9w1qI+1XBv0KJNoHCOsmIoBBWKONqBpe+vMTlry5TsU1F3Ke64xrgKvMI78Zg\ngF27jHcx+b//MzaFw4bBwIFs3LqVTaNH02PJEoIGDLB0pkKIUkTrfYs0gkJYsezUbK4uu0rUJ1HY\nOtviPtWdR555BJtyMth/V+npsGEDoa+9xorTp/EF/gPMrFyZQ7a2DB4+nGEff2zpLIUQpYDW+xY7\nSyfwIIKDg+VkkTvinOe0ko9W4vnz5+Pn56eZfIo63rFvBzSCLse6ELshll/f+pW0l9P416v/oubz\nNdl1aJfZ9+c8Z+n8LRo/9RS1K1XisU2biJ87l+3A+fh4OnfrxtC337Z8fhqJIyIimDJlimby0Up8\n5/eSpfNJOZHC/iX7oafsL1qLtU5GBEsJvV5v2vnEbWWxLkkHk4ieF83NtTepPqw67lPc81w2QinF\n2KFjWbx8sRxKBjb+8gubBg0iGqjt7ExPPz+Cjh+H4cNh0iRo0MDSKVpUWfweuhdaqkvq+VQiukTw\n6LuPUmNkDYvmoqW6aIXW+xZpBIUopdIvpXPpi0tcDrmMaxdXPF7xoFKHSqz9ZS2ho0MZvmQ4vQb0\nsnSalqXXE/Kf/1CncmW6N23K5uPHiYqNZezzz8PBg7B4MbRvbzzxJDCwwDOOhbCU9EvpHOx8EI9X\nPKj9Ym1LpyPM0HrfIo2gEKVcVnIWMUtjWDRzEVtTtlIvqx5jGMN3lb/jnO05hgwfwgsfv2DpNLXp\n1i3jJWg+/RRsbWHyZHj2WXCSC/MKy8u4lkFElwhqjKpBndfqWDodUQCt9y02lk5AFA1rmYtQ0qQu\nYOdih/tEd2bfmM2EKRNQKA5xCJ2Tjte+fI3xH423dIqaYHZfcXaG55+H//0PPv4YVq2CunWNt7q7\nfLnEc7QE+R4yTwt1OT78ONUGVdNUE6iFuoj7Y5WNYHBwsGln0+v1eXY8iSXOHUdERGgqH0vGCTsT\nOLT/EJfsLvGb/W8kXUoi7D9hrP10rSby03Ss00G3buinTUP/0UfG+xw3b46+a1f0X31l+fyKMY6I\niNBUPhLfjuPGxXEh8IJm8tHL/nLXWKvk0LAQZciCOQvwbOTJk/2f5Oe3fubgpwcZ9/I46r5dFxs7\nq/y70HLi4+Gbb+Dzz6FWLeM8wv79wc4qL8YghCgmWu9bpBEUogxLj0nnxIgTZKdk4/WDF451HS2d\nkvXJyoLffoP58+H8eZg4EZ57DipXtnRmQggN0HrfIkMApYQ1DD9bgtTFvJy6ONRwwGejD1X7VeVA\n6wNc+79rlk3Mgh54X7GzM44E/vknrFkDx45B/fowfjwcP16kOVqCfA+ZV9J10XIjkZvsL9ZHGkEh\nyjidjY46r9bBe50356af4+RzJ8lOybZ0WtapRQv47jtjA1ijhvGSMz16wIYNxlvcCfEAlEFxYtQJ\nrv1Sdv9QE8VHDg0LIUyykrI4/eJpksKT8FrhhYuvi6VTsm7p6bBihfGwcWoqvPQSjBgBLlJXcW+U\nUpyeeJqUIyn4bPTB1tnW0imJ+6T1vkUaQSFEPjHLYjg79Sx1365L7Ym15Q4kD0sp2LHD2BD++SeM\nHg0TJhgvRSNEAZRSnHvjHPF/xOO7zRe7inIikjXSet9ilYeG5fIx+WOph/l4/vz5mspHK3HOcwW9\nXmN4Dfz3+LPui3Us6biEjBsZmsq/OOI7a1Ok69fpoHNn9C+9hP7zzyE7G1q0QN+lizH+55eEluqR\nE8+fP19T+Wglzvm6OLe3fex2ov4vCp+NPthVtNPU5y8olv2l4Di3ffv2ERgYCMCZM2fo1KkTnTt3\n5sUXXzQ1jSEhIbRu3Zr27duzbt06s+spEsrKWGHKJSIsLMzSKWiS1MW8e61Ldnq2OjPtjNpVe5eK\n3RZbvElZWInvK4mJSn3+uVINGijVsqVS33+vVHp6yeZwD+R7yLzirktGbIY6GHBQpV1JK9btFDXZ\nX/K7s2+ZO3eu8vb2Vu3bt1dKKdWnTx+1fft2pZRS48ePV7/++qu6cuWK8vb2VhkZGSohIUF5e3ur\n9GL6+SCHhoUQhYrdHMuJ4BPUGFUDz9me2JSzyoMJ2mQwGE8mmT8fjh6FF16AcePgkUcsnZkQogjc\n2besWrUKHx8fhg8fzp49e3B3dyc6OhqA3377jc2bNxMUFMT69etZuHAhAP379+fNN9+kVatWRZ6f\n/DQXQhSqcvfKtDrYiqQDSUR0jiD1fKqlUyo9bGygVy/YsgU2b4aoKGjc2DiP8NAhS2cnhChi/fv3\nxy7XhedzN4kVKlQgISGBxMREKlWqlO/54iCNYClR0DyEsk7qYt6D1MW+uj0+632oNqgaf7f9m2s/\nla5LWWhiX2neHBYtgtOnoWFDY4MYGAirVxvnFVqAJuqiQVIX86QuxhrMnj3b9CiMjc3tViwxMRFX\nV1cqVqxIUlKS6fmkpCTc3NyKI11pBIUQ905no8Njqgc+G3w4P/M8J8ackGsOFoeqVWH6dOOdSsaN\ng/ffNzaG8+dDYqKlsxPFIO1CmqVTEEUkICDgvhpBf39/tm/fDsCGDRvo3Lkzbdq0YceOHaSnp5OQ\nkCInMXIAACAASURBVMDx48dp3rx5seQrcwSFEA8kKymL05NOk7g3Ea8VXlTwq2DplEq3vXvh009h\n0yYYPhwmTYIGDSydlSgC8TviOTrgKC0PtMTRQ27zWNqY61siIyN59tln2b17N6dPn+a5554jIyMD\nLy8vQkJC0Ol0LF68mEWLFmEwGJgxYwb9+vUrnvykERRCPIyry69yZsoZ6s6sS+2X5JqDxS46Gr78\nEkJCoH17mDwZHn/ceIkaYXUS9ydypNcRvH70wu2J4jn0JyxL632LHBouJWRehnlSF/OKsi7Vh1an\nxd4WXF1+lSN9jpBxPaPI1l2SrGZfcXeH996DCxegTx/j3Up8fOCbb4x3LyliVlOXElYUdUk+nMyR\n3kdo/E3jUtMEyv5ifayyEZQLSkt8r3FERISm8imtsVN9J/x3+nOk4hEWNV1E3LY4TeVXKmNnZ/QN\nG6L/4gv45BP49Vf0NWuiHz4cLl8usu1FRERo4/OWsvjWqVuEPx5O5rhMqvapavF8iiqW/aXgWKvk\n0LAQokjFbvnnmoMjauD5jlxzsESdOgWffw7Ll0PPnsbDxm3aWDorYUbquVQS9yVSfUh1S6ciipnW\n+xZpBIUQRS7jWgYngk+QeTMTrx+9cKrnZOmUypb4ePj2W2NTWLMmTJkC/fuDndyrVoiSpvW+Rf5U\nLyWsYfjZEqQu5hV3Xewfscd7rTePDHmEv9v+zdUfrxbr9opCqdpXXF1h6lQ4cwamTYMFC+DRR2Hu\nXIiNva9Vlaq6FCGpi3lSF+sjjaAQoljobHR4TPHAZ5MPkbMjOTHqBFnJWZZOq2yxtYV+/WD7dvjt\nNzh+HOrXh/Hj4dgxS2cnhNAAOTQshCh2WclZnHnpDAk7E4zXHGwh1xy0mKtX4auvYOFC8PU1HjYO\nCjLe6k4Ui+yUbK6tuEaN0TXk8kplkNb7FmkEhRAl5uqPVznz0hnqvFkH98nu6Gzkl6LFpKfDTz8Z\n71Zy65bxMjQjRoCLi6UzK1Wy07I50vsIjnUcafxNY2kEyyCt9y3yJ2ApIfMyzJO6mGepulQfUp0W\n+1pw7adrHOl9hIxr2rnmYJnbVxwcjI3fgQPG+xtv2waensY5hStWwOzZKJ2OcTodatYsmD0bylqN\n7uJe9hdDpoFjg45Rrmo5GoeUjSawzH0flQLSCAohSpRTPSf8d/jj4udCuH84sVvu7+QFUcR0Oujc\nGVauhPBwUAomTID//Y9NwE1gs4+PsREMCLBsrlZEZSuODz8OQNNlTdHZlv4mUFgnOTQshLCYuG1x\nHB95nOpDq/Pou49iYy9/m2pB6GefsWLOHHxjYvgPMLNmTQ65uTH4pZcYNm6cpdOzCufePEfivkS8\n13lj62hr6XSEBWm9b5GfukIIi3F7wo1WB1uRcjSFg50Oknq26G+RJu7f0EmTmPDZZxgAHWC4eZOJ\ncXEMLVcOMrRzOF/Lak+oTfM1zaUJFJonjWApIfMyzJO6mKeluthXs8f7d2+qD6/O3+3+5upyy1xz\nUEs1sTSdTodOpyMNGASkOjigGzcO3YoVxsvPzJ8PycmWTtOiCttfHGo7YOdS9i7gLd9H1scq99Lg\n4GCCg4MJCAgw7XQB/8xdKatxDq3ko5U4576XWslHK3EOreQTEBCA+yR3DjsdZuX0lQRuDqThFw3Z\neWCnZvIrUzEQ9dVX1OzShToODnhVqkTUjh3oe/eGAQMI2LYN3nsP/ZNPQr9+BPTtq638JbZYHBER\noal8tBBrncwRFEJoSnZKNqcnnyZhewJNf2xKxVYVLZ2SMOfUKfjwQ+NJJiNHGu9k4uFh6ayE0Byt\n9y02lk5ACCFysy1vS5PFTXj0v49y5MkjRH0chTJo94domdWoEYSEwJEjxjuY+PnB6NFw4oSlMytx\nV765woU5FyydhhAPRBrBUsJahqBLmtTFPGuoyyNPP0KLv1pw/ZfrHH7yMBlXi/ckBWuoiSUUWpfa\nteGjj+D0aahXD7p0gQEDYP/+EsnPUnLqcnXFVc6/dZ5qA6pZNiGNkO8j6yONoBBCs5w8nfD7048K\nrSoYrzm4Sa45qFmVK8PMmXDuHAT8P3v3HR5FuT1w/DubTgmhS0+BACEEAoh0wkV/lyIqxQIKiV3R\niyCi4FXAiqIiFq7l0kHxohdREBRBFkFqgBBCCBAIEDqhhPS28/tjb0LJRjchuzOzez7P4/PkJDsz\nJ8eXzdmZd96JgmHDoG9fWLvWujahC1FVlSVfLuH8D+dJHptMxC8RVAmtonVaQlSIzBEUQhjCpfWX\nSBqVRL0H6hH0lqw5qHsFBbBkCbz7Lvj5wcSJMHiw9TKywa38biWLohfR2aMzj697HP9bZR6rKJve\n+xZpBIUQhpGfls+BRw+QdzKPsCVhVGkhZ2F0z2KBFStg2jS4dAlefBEeesj6iDuDmf/FfBZ9vIjg\ngmBGHBrB4saLOep/lJFjRhLzZIzW6Qmd0nvfIh+pXYTMy7BN6mKbUeviXceb8OXhNHi4Abu77ebM\nwjOVtm+j1sTRbrouJhPcfTds2QJffAHffmtdi3DGDMOtRRj9RDRjp46lKLcIBQVVURn32jiin4jW\nOjXdkH9HxiONoBDCUBRFodEzjWi3rh3H3znO/pH7KbxSqHVa4q8oinXu4M8/W88QbtsGQUEwZQqk\npWmdnV2KF9rOvpzNrGazyLqcVfI9IYxKLg0LIQyrKLuI5HHJXFp3ibAlYTJXy2gOHbLecfzttzBy\nJIwfD02bap3Vn5o1bRaBoYEMGDKAVctWcezQMUZPHK11WkLH9N63SCMohDC8c9+d49DoQzR5oQlN\nXmiCYpIzNIZy6pT1sXVz5sCgQdZ5hGFhWmclRKXQe98il4ZdhMzLsE3qYpur1aXesHp03NGRtB/T\niO8XT96ZvHLvw9VqUlmcUpeGDWH6dEhOhhYtoE8f6x3G27Y5/th2yjmcQ8HlgpJYxottUhfjkUZQ\nCOESfJv50t7cHv+u/uyM3MmF1Re0TkmUV82a8M9/QkqKdQ3C+++Hv/0N1qzRdC1CVVXZ/9B+Lvwo\nY0q4Hrk0LIRwOZc3XGb/yP3UHVaX4GnBmHzkM68hFRTAf/4D77xjXW5m4kQYMsTpaxGe/fosqTNS\n6bi9o0w7EOWm975FV43gunXr+M9//kN2djYvvvgiERERpV6j94IKIfSh4EIBBx47QO6xXMK+CZMn\nPxiZxQI//WRdizAtzTqHcORIp6xFWJRdxPZW22n9dWsCegQ4/HjC9ei9b9HVx+ScnBy+/PJLXnjh\nBdasWaN1OoYi8zJsk7rY5g518artRZtlbWjweAN2d9/N6fmn//TN2B1qUhG6qIvJZL2J5I8/YPZs\nWLbM+lzj99+HjAyHHjr1/VT8u/qXagJ1URcdkroYj64awTvvvJOsrCw+/vhjYmJitE5HCGFwiqLQ\n6OlGtPutHanvpbL/QVlz0NAUBXr1glWrrGcId+60NoSvvgrnz1f64QouFXDi4xMEvxtc6fsWQi8c\nfml427ZtTJw4kfXr12OxWBg9ejTx8fH4+Pgwe/ZsQkJCePXVV0lOTuajjz5i4sSJvP766zRu3Nh2\nwjo/xSqE0Kei7CIOjz/MxTUXCfs6DP/bZM1Bl3D4MLz3Hixdan103fjx0KxZpe0+72QePo2M9zg8\noR9671sc2ghOnz6dxYsXU61aNTZv3syyZctYuXIlc+fOZdu2bUybNo3ly5eXvD46Opq0tDRq1arF\nPffcw9ChQ0snrPOCCiH07fyy8xx86iCNn29M0xebyuR/V3H6NHz0Efz73zBwILz0ErRpo3VWQui+\nb3HopeHmzZuzbNmykgJs2rSJfv36AXDbbbcRGxt73esXLFjATz/9xKJFi2w2gaJsMi/DNqmLbe5c\nl7pD6tIxtiMXV11kz//tIe9UHqqq8uiIR3X9Zq0Vw4yVBg2sdxcfPgytW1uXnyl+xrEDGKYuTiZ1\nMR6HNoJDhgzB09OzJM7IyMDf/+rlGA8PDywWiyNTEEKIUnyb+tLut3b4NPFhW+g23jO9R/KSZObf\nP5+UqSlcMl/SOkVRUQEBMGmSdS3Cv/8dRoy4+oxjafSFKMXzr19Sefz9/cm45g4vi8WCyVT+XjQm\nJobAwEAAAgICaN++PVFRUcDVTyMSS1zMbDbrJh+J9RObPE0sqLmAn6v9THhWOFOZyvub3+eT7Z8w\npsEYYqJidJWvlnExveRjV+znhzksDGbPJurMGZgwAfOzz8KIEURNmQIeHje1/6ioKH39vjqKi+kl\nH61jvXP4zSJHjx5l+PDhbNmyhWXLlrFixQrmzZvH1q1beeONN/jpp5/KtT+9X2sXQhiHqqqs/G4l\n39z3DY/zOHNrzOW+2fcxcOhAFEXmDroUVbXebTxtGpw5Y12LcNQo8PW97mXHph3Du743DR5poFGi\nwtXovW8xOeMgxW+ogwcPxtfXl+7duzN+/Hg+/PBDZxzeLRjlk4ezSV1sk7pYKYqCoijkk88UppB9\nJZvL6y9LE3gNlxkrimK9iWTTJpg/H3780br0zHvvwZUrgPUO4dT3Uwno89cLR7tMXSqZ1MV4HH5p\nODAwkM2bNwPWN93PPvvspvcZExNDTEyMnJqn9D86veSjlzguLk5X+eglLqaXfLSKV8xcwfJFyxlw\n7wB8/HzYvX83K75YQc9mPWn2YjPN89NDHBcXp6t8Ki1euRLznDmwZAlR774LTz7Jls29oJ8PfkF+\n2udn0Nhlx8tNxHqnq0fM2UPvp1iFEMaW/kc6CfckEPFLBNU7VNc6HeEMR45w5cU5JCzrSOfHfsfz\n5bHwv3noQtwsvfctJq0TEEIIPanRvQahX4ayd9BeclJytE5HOIEaFETy6aEEfdAGz9p+0LGj9VnG\nCQlapyaEw0kj6CKMcgra2aQutkldSru2JnUH16XppKbE94+n4EKBdknpgDuMFUuOhZp31OSW50Kt\nN5McOQLh4XDHHdZnHG/ebH2h2QxTp6IqCk8qCuqUKTB1qvX7AnCP8eJqDNkIxsTElAw2s9l83cCT\nWOJr47i4OF3lI7Fx4sbPNiY5Mpl5veZRlFOkeT5axcXzbPWSjyNijyoeBE0NYsPvG6w/r1EDXnoJ\n8/z5mFu0sD66rlcvzNu2Ye7dm1+AC8B7JhPmqCi4Zk6YHn4fLWN3GC8VjfVK5ggKIUQZVIvK/pH7\nsWRbaPNdGxQPuZvYLRUWwrffsnjCBL5JS6NdXh5vAq+0aMEeLy8eGDOGh558UusshU7pvW8x5BlB\nIYRwBsWk0GpuKwrTCzn03CFdv5kLB/L0hOHDefD4cZ55/nksgAJY0tJ49pVXePCJJ7TOUIgKk0bQ\nRRjh9LMWpC62SV1KK6smJh8T4d+Hk/57OqnvpTo3KR2QsXKVYjKhdOhALnAvkHPlCsqzz6J89BFk\nZWmdni7IeDEeQzaCMkdQYntjmSMocWXEnjU8abuqLSs+WMGyV5Zpno8zY1ed85W5NxPzl+Xffu0v\nv9APGA00fPVV1vbqZV2kOjgY86OPYl65Uhe/n1axq46Xyoj1SuYICiGEnbL2ZRH3tzjCvg6jZt+a\nWqcjKkhVVXb33E2DRxpU7FFyxU+eufZv0f798M478NNP8MQTMHYs1KtXOQkLQ9N73yKNoBBClMPl\nDZfZd+8+2q1tR7WIalqnIyrg3NJzHH/nOB13dCzfDUBms/W/G11z5zApKdbH1n3zjXUtwhdegCZN\nbj5pYVh671sMeWlYlGaE089akLrYJnUpzd6aBPQOoMUnLdg7cC+5x3Mdm5QOuNpYKcop4vCLh2n+\nYfPy3wUeFWVdN3DqVOuyMf/7uqQJBAgKgn/9C/btA29vaN8eHnsMkpMr61fQNVcbL+7AkI2gzBGU\n2N5Y5ghK7Ii43v31aDy2MfN6zWPtirWa5+PI2NXmfG38x0aqd6pOQO8Axx6vQQPMAwdinjsXGjeG\nrl0x/+1v1lhH9ZDx4rxYr+TSsBBCVICqqiSPSyZzdybt1rTD5GPIz9VuxVJgYUebHUT8HIFfsJ9z\nD56RAZ9/DjNmwK23wj//Cbfd5twchCb03rdIIyiEEBWkWlQS708EE4QtCUMxyYLTemfJt2Dy1rBp\nz8mBefNg+nQICbE2hH36XL0BRbgcvfct8hHWRRjh9LMWpC62SV1Kq0hNFJNCq0WtyD+dz+EJhys/\nKR1wtbFSWU1ghevi5wejR8OhQzBqlPXrrl1hxYrr70I2KFcbL+5AGkEhhLgJHr4ehC8P5+Lqi6TO\ndL8Fp0UFeXlBdLT1ppIXXoDJk6FdO+vdxkVFWmcn3IhcGhZCiEqQeyyXXd130fzD5tS7V9aPE+Wk\nqvDzz/DWW3D2LEycaF1+xttb68zETdJ732LIM4Jy17DEEkust9i3mS+Xp17mP4//h8u/X9Y8H4n/\nF681o1pU/eRTVqwomP38ML/xBsyZA0uXYm7UCPM//gHZ2drnJ/FNx8UsFguPPPIIPXr0oFevXhw4\ncIDk5OSSePTo0c5tHFWDMWDKTrF+/XqtU9AlqYttUpfSKqsmF9ZcUDfV26Rm7suslP1pzehj5ehb\nR9XkCcmVvl+n1GXHDlUdPFhV69dX1bffVtXLlx1/zJtk9PHiCDf2LatXr1bvu+8+VVVV9ddff1WH\nDBmi3nXXXeqGDRtUVVXVp556Sv3++++dlp8hzwgKIYRe1bqjFiHvhxA/IJ68U3lap+PW8k7nkfpB\nKg2fbKh1KhXTqRMsWwbr1kFiovUu41degfPntc5M3AQ/Pz/S09NRVZX09HS8vb3ZuXMnvXr1AqB/\n//6sXbv2L/ZSeWSOoBBCOMCxacc49805IjdG4unvqXU6binpkSS86noR8m6I1qlUjiNHrMvOLF1q\nvdFk/HjrYtVC127sWwoLC7n99ts5ffo0Fy5cYMWKFQwbNoyTJ08C8NtvvzFv3jwWLVrklPzk3UkI\nIRyg6cSm5KXmkTAkgYhVEdquXeeGMnZmcHH1RTof6Kx1KpUnONi6KPXkyfDBBxARAcOGwYsvQvPm\nWmcn/qesuYHFpk+fTvfu3Xnrrbc4ceIEffr0oaCgoOTnGRkZBAQEOCFTK3lnchF/NujcmdTFNqlL\naZVdE0VRaPFJCzyqeXDg0QOGvZJhxLGi/u+pL4GvBzrsbKymdWnY0NoIHjwIt9wCXbrAgw9CQoJ2\nOf2PEcdLZYuKimLq1Kkl/90oKysLf39/AGrWrElhYSGRkZFs2LABgNWrV5dcJnYGaQSFEMJBFA+F\nsK/DyEnOIeXlFK3TcR8qNHy6IQ0eaaB1Jo5Vpw68/rr1knFEBNx+O9xzD2zfrnVm4k9MmDCBrVu3\n0rNnT/r27cu0adP49NNPmTJlCt26daOwsJBhw4Y5LR9DzhGMjo4mJiaGqKiokk8fUVFRABJLLLHE\nuou7hXdjd7fdHB9wnLr31NU8H4ldNP7lF1i1iqjlyyE0FPOAAdC+PVF9+ugjPzeN+/Tpo+srAoZs\nBA2WshBCkHMkh909dhP6WSh17q6jdTrCleXnw1dfwTvvQO3a1ucZDxggzzPWiN77FpPWCYjKUfzJ\nQ1xP6mKb1KU0R9fEL9iP8B/DOfDYAdK3pDv0WJVJxoptuq6Ltzc8/LB1yZmxY62NYGSk9W5jBz++\nTtd1cWHFS9FkZGSwcOFCLl26ZPe20ggKIYST+Hfyp9XCViQMTiD7YLbW6QhX5+EB990Hu3dbH103\ncyaEhcG8edazhsJlPPDAA/z444+8+OKLbN68mUceecTubeXSsBBCONnpOac59tYxIjdH4nOLj9bp\nuISM3RlkJWRxy8hbtE5Fv1QVNmywNoUHD8KECfDoo+Dnp3VmLs0ZfUvPnj3ZuHEjUVFRmM1mbr/9\ndrsXpZYzgkII4WQNHm1A/VH12XvnXgozC7VOx/BUVSV5bDKWHIvWqeibokBUFPz6K3z7Laxda12b\n8N134coVrbMTN6GgoIBly5bRpk0bzp8/T0ZGht3bSiPoImRehm1SF9ukLqU5uyaBUwKp1r4aifcm\nYinQbwNjhLGStiyNwsuFNHjUecvFGKEuf6pzZ1i+3NoUxsdbG8LJkyEt7aZ2a/i6GNSLL77IN998\nw6RJk/jkk0949dVX7d5WGkEhhNCAoiiEfhYKChx86qBMeamgotwiDk84TPMPm6N4yF2x5RYebr3D\neNs2OHMGQkOtj647dUrrzEQ5DBkyhKVLl9K4cWNef/117rzzTru3NWQjGBMTU/Kpw2w2X/cJxF3j\na9ct0kM+eomLv6eXfPQSy3gpHRfPrXHm8X//43fO/+M8mXsyOfraUV3Vozi+lh7yuTHe+NxGqkZU\npebfajr1+FqMF4fGISGYR4zA/MUXYLFAeDjmu+7C/PXX5drftXT1++kgdqS3336bgIAAGjRoQIMG\nDWjYsKHd28rNIkIIobH8s/ns6raLppOa0vAx+9/A3Z2qquy9cy/NP2pOleZVtE7HtZw/Dx99ZH22\ncf/+MHEitGmjdVaG5Iy+JSIigq1bt1KlSvn/HRjyjKAozVmfOoxG6mKb1KU0LWviXd+biNURpLyS\nwoVVFzTLwxY9jxVFUYj4KUKTJlDPdakUdevCm2/C4cPWJWf69oUhQyA2tvRrzWaYOhUUBbOiWL+e\nOtX6feEUwcHB+Pr6VmhbxzyNWwghRLlUCa1C+PfhJNyVQNtVbfG/1V/rlISAGjVg0iR47jmYPRsG\nD7Y2hi+/DL16Xb0TOSoKXnvNus3UqRom7J7y8vJo27Ytbdu2RVEUFEXh62su6/8ZuTQshBA6kvZD\nGgefPkjkxkj8QmR9N6Ez+fmwaJH18XX161sbwv79QVFQFYX3gAkWC4o8zq6EM/oWs9lcqua9e/e2\na1u5NCyEEDpS5+46NHu1GfH948k/L09/EDrj7W1dhDopCZ591jp3sEMH+PZbfgFOA2uWLdM6S7cT\nGRnJypUreffdd1m+fDlt27a1e1tpBF2Ey89XqSCpi21Sl9L0VJNGTzei7rC67B20l6Jsxz4b9q/o\nqS4AljyLLhpkvdXF6Tw84IEHYM8eFnfrxp2jRrERuAv4fdIk7mzThsVffKF1lm7jkUceoUmTJrz1\n1ls0a9aMmJgYu7eVRlAIIXQo6K0gqoRWIXF4IpZC/S447WwnPjpB8nPJWqchiikKD376Kc8sWIAF\nUADLkSM8GxXFgw8/rHV2buPChQuMGTOGyMhIxo4dy8WLF+3eVhpBF1G8Lpy4ntTFNqlLaXqriaIo\ntJzdEku2heR/JGs2N1pPdck/m8/x6ccJfC1Q61R0VRetKYqCYjKRC/wI5Pj4oPz+O0qrVtYbTPK1\nP4Pr6nJzczl9+jQAZ86cwWKx/8OjNIJCCKFTJm8Tbf7bhvQt6RyfdlzrdDSX8moKt0TfQpUWsmag\n3qQeOkQ/4AOg/8KFpD74ICxcCEuXQosW1vUI8/K0TtNlvfHGG3Tv3p327dvTrVs33njjDbu3lbuG\nXYTZbJZPqDZIXWyTupSm55rkncpjV7ddBL0exC2jbnHqsfVSl4y4DOL7xdM5qTNeAV5ap6ObuuiK\nomAGom78G71lC7zxBuzdCy+9BI89BhVc886InNm3pKWlUadOnXJtI2cEhRBC53wa+hCxKoLDEw5z\n8Vf75/64ksMvHCZwaqAumkBRTl27wqpVsGwZrFkDISHWp5ZkZ2udmeE988wzAHTt2pWuXbsyaNAg\nunbtSrdu3ezeh5wRFEIIg7i88TL7hu4jYk0E1dtX1zodp8qMz6RKWBVMnnL+QreK17H7q7/Ru3ZZ\nzxBu3QovvABPPQVVqzo+P404sm85e/Ys9evX59ChQ3h5Xf2QdOnSJSIjI+3Lz4iNYHR0NDExMSUP\n/YarE3clllhiiV05vrThEvX/XZ8Of3Rga8pWzfORWOIoa4D56FFrHBho/XlAALRvX/b2s2fDokVE\nJSXB889jjogAPz/tf59Kjvv06eOwRvD06dNcuXKF6OhoFi5cCEBRURHR0dFs377drn0YshE0WMpO\nYTabSwafuErqYpvUpTQj1eTERyc49fkpIv+IxKuWYy+VGqkuziR1sa1CdUlIgLfegnXrYOxY60LV\n/q7ziEVH9i3ff/89H3/8MXFxcbRv3x4Ak8lUrhtG5FnDQghhMI2fa0zu8VwS7k4g4tcIPHw9tE5J\niIoLD4clS2D/fmtDGBIC//gHjBkDAQFaZ6drgwcPZvDgwfz0008MHDiwQvuQM4JCCGFAqkUlcUQi\naqFKm6VtUEzybFfhIg4ehLffhpUr4Zln4LnnoFYtrbOqMGf0LVu2bGHevHkUFhZisVg4ffo0v/zy\ni13bmhyamRBCCIdQTAqtF7SmIK2A5Oe1W3DaUTLjM0keL08QcUuhoTB/PmzbBidPWtch/Oc/IS1N\n68x06+mnn6ZPnz6kp6cTGBjIbbfdZve20gi6iOJJqeJ6UhfbpC6lGbEmJh8T4cvDubT2EidmnHDI\nMbSoi6qqJI9Nxi/Ez+nHtpcRx4szVGpdQkKsTyaJjbU2gaGh1nUIz52rvGO4iDp16jB8+HCqV6/O\n1KlTiY2NtXtbaQSFEMLAvAK8iFgdwYmZJzj7zVmt06kUF368QP7ZfBo80UDrVIQeBAXBF19AXBxk\nZkKrVtZlZ86c0Toz3fDw8CAhIYGcnBySkpJITU21e1uZIyiEEC4gc28me/ruIWxpGDWjamqdToVZ\n8ixsb7Od0H+FUuv/jDsvTDjQyZPw7ruweDGMGgUvvggNG2qdVZmc0bfs27ePffv20bBhQ5577jke\neughxo0bZ19+0ggKIYRruPTbJRIfSKTdunZUa1tN63Qq5Pj7x0nfkE7bFW21TkXo3enT8N571vmE\nI0bAxInQuLHWWZXijL7l+eefZ8aMGRXaVi4NuwiZr2Kb1MU2qUtprlCTmn+rSfOZzdk7cC+5J3Ir\nZZ/OrkvhpUJC3g9x6jErwhXGiyM4tS4NGsCMGdZlZ6pUgYgIePppOHbMeTnoRGJiIpcuXarQ8ibm\noAAAIABJREFUttIICiGEC6k/oj6Nnm3E3gF7KUwv1Dqdcgt+K5gqLatonYYwkvr1Yfp0OHDAuu5g\nhw7w+ONw5IjWmTnN/v37qVOnDvXr16dBgwY0LMelcrk0LIQQLkZVVZLHJJO1L4uI1RGYfOQzv3Aj\nFy7AzJnwr3/B3XfDyy9D8+aapaP3vqXc7w45OTmOyEMIIUQlURSF5jOb4xngSdLDSagW/f4REqLS\n1a4Nb7wBycnQtCl06WK9qeTAAa0zc5iEhAR69uxJeHg477//PitXrrR72zIbwaNHj/KPf/yDKVOm\nkJ2dDcCqVasIDw+/+YxFpZP5KrZJXWyTupTmajVRPBRaf9Wa3GO5HJlY8UtkrlaXyiJ1sU1XdalZ\nE6ZOhcOHrWsQ9uhhvakkMVHrzCrdmDFjmDt3LnXr1mX48OFMmTLF7m3LbASHDx9O27ZtKSgoYPLk\nyUyaNInnn3+eBQsWVErStuzcuZOHH36YmJgYzsmCkUIIcVM8/Dxo+2Nb0n5M48Qnjllw+mZZ8i2k\nb0nXOg3hymrUgFdesTaEERHQpw/cfz/s3at1ZpWqRYsWADRq1Ah/f3+7tytzjmCPHj3YtGkTAEFB\nQfTs2ZMvv/wSX1/fSkjXts2bN9OmTRvWrFmDt7c3d999d+mEdX6tXQgh9CYnJYfdPXbT4pMW1B1S\nV+t0rpP6QSqX1l8iYmWE1qkId5GZCZ99Bh98AN27w6uvQvv2DjucM/qWYcOGcfvttzN37lzGjRvH\n0qVL+f777+3atswzgl5eXiVf16pVi/nz5zu0CQTo1q0biYmJvP/++7R34P8UIYRwJ35BfrRd0ZaD\nTx0k/Q/9nH3LP5/P8XeOG2K5GOFCqlWDCROsdxX36AEDBlhvKtm5U+vMKmzu3LmkpKRQp04dYmNj\nmTNnjt3b2nWziL+/PyZTxe4627ZtG3369AHAYrHw1FNP0a1bN/r06cPhw4cBmDx5MsOHD2fHjh10\n6tSJ1atXV3hhRHelq3kZOiJ1sU3qUpqr16R6h+q0XtSahKEJZCVl2b2dI+tydPJR6j1Yj6qtqjrs\nGI7i6uOlogxVlypVYNw46yXjvn3hrrvgzjth+3atMyu3jz/+mHfffZdVq1bxwQcf8N5779m9rWdZ\nP/jjjz9o0MD6nMeLFy+WfK0oCqdOnbJr59OnT2fx4sVUq2Zd4X758uXk5+ezefNmtm3bxvjx41m+\nfDmvv/46AOvXr+eRRx7B29ubJ5980u5fQgghxF+r9fdaBL8TzN7+e4ncHIlPAx/Ncsncm8n5/56n\nc1JnzXIQAgA/PxgzBp54AubOhWHDICwMJk+Gbt20zu5PzZkzh9mzZ5OYmMhPP/0EWE+65efnM23a\nNLv24dB1BJctW0ZERAQjR45ky5YtPP/883Tp0oX77rsPgMaNG3PiRPkmMMscQSGEuDlH3zxK2rI0\n2m9oj2f1Ms8HONS++/dRo2cNGj+rv0eCCTeXlwcLFsDbb1vXH5wyBXr2LN8+zGbrf6+9hgIO61vy\n8vI4ffo0b731Fq+88gqqqmIymahfvz4+PvZ90Cvzeu+8efNKvt63b1/J16+99prdCQ4ZMgRPz6tv\nMhkZGdfdyeLh4YHFYrF7f0IIIW5es382o3qn6uwbtg9LgTbvwS2/bEnDJ+1/+oEQTuPjYz07ePAg\nDB8O0dHWO43Xrwd7G7qoKOvSNQ527Ngx8vPzeeGFF8jLyyM/P5/c3FyOleMxe2V+FFy4cCEPP/ww\nAM8++yzr168HrNf/y7M+zbX8/f3JyMgoiS0WS4XmHsbExBAYGAhAQEAA7du3JyoqqiQ/wO3i4u/p\nJR+9xDNnzpTxYSMu/p5e8tFDfGNttM7H0XGLf7Vgfs/5xA2KY9TqUSiKYvP1cXFxjB07ttKP71nD\nU1f1KG/sbuPF3thR40WTePNmCAkh6sAB+PprzA89BLVrEzVjBvTti3nDBrv250hPPPEEiqLY/Flx\n3/aX1DJERUX95df2SElJUbt06aKqqqr+97//VWNiYlRVVdUtW7aoAwYMKNe+VFVV/yRlt7Z+/Xqt\nU9AlqYttUpfS3LEmhZmFamznWPXIK0fKfI071sUeUhfbXLouBQWqunixqrZsqapdu6rq6tWqarH8\n+Tag+77FVGlt6Z8o7lYHDx6Mr68v3bt3Z/z48Xz44YfOOLxbKP4EIq4ndbFN6lKaO9bEo6oHbVe0\n5dw35zj1xSmbr3HHuthD6mKbS9fF0xMefBD27bPeXDJ+PNx2G6xcaf8lYx0q89LwxYsXWbNmDaqq\nlvq6PAIDA9m8eTNgbQg/++yzm8sY66XhmJiY607P6+ZUssQSSyyxweLOqzsT1zOOHRd2UKNbDccd\nb70ZFO1/X4klvun4gQcw16sHGzcS9fLLMGUK5sGDoXt3ov63ZF7x6/WuzLuGY2JirrvunJOTA4Cf\nn991N5I4m9w1bJvZbC4ZrOIqqYttUpfS3L0mV7ZfYe/AvbRd2Rb/267e1FdZdclMyCT5uWTarW1X\n5pwmI3H38VIWt6yLxQI//ACvv249M/jqqzB4MJhMqIqCCcfdNVwZTGX9YOzYsZw/fx6TycSIESNY\ns2YNv/76a8ni0EIIIVyHf2d/Ws5rScI9CWQfyq7UfauqyuFxh6lzTx2XaAKFuI7JZG38du2CN96A\nd96xPtP4P//hFyelsGDBAlq3bk1QUBBBQUEEBwfbvW2ZZwS7du3K66+/zsWLF3nkkUfYvXs39erV\n4+9//zvbtm2rtOTLS1EUoqOj5dKwxBJLLLED4rQVaTT+oTEdNndgc+LmStl/eGY4RyYcIfuTbPDU\n1+8rscSVHvfuzeIxY5j1xReEFBTwFY4/IxgWFsaPP/5I48ZX1+W097HAZTaCUVFRJb9Ut27dSub5\n3X777axdu/YmU644uTQshBCOlTgqkcvrLpN3Ko9v+IaXJr+EoigERAVQM6pmufZlybewI3wHzT9q\nTu3+tR2UsRD6oqoqP3/7Lb/ffz/v4PhGcNCgQaxYsaJC25rK+sG1p++vXZ26qKioQgcSjlXctIvr\nSV1sk7qUJjW5qvWC1tT6v1rsYAdJJLE/Yj9BU4PK3QQCnJx1Er8QP5drAmW82CZ1sVIUBcVkItdJ\nx/Pz86Nfv35MnDiRSZMm8fLLL9u9bZl3De/bt48RI0agqiqJiYkMHz4cgMTExJvPWAghhG4t+HIB\ni7YtogENuId7WD5pOTMmz2DkmJHEPBlTrn35NPYhZEaIYxIVQsdSDx2iHzDTCccaMGBAhefflnlp\n2Gw227wMqygKvXv3rtDBKoPMEZRYYokldmzcu3dvVn63ko/u+4g7uZO4enEMmzWMqrWroiiK5vlJ\nLLFh4j596IPjLw0XFBSwY8cOCgoKUFWVU6dOMWLECLu2LbMR1CuZIyiEEI638ruVLLh3AQCqohI9\nP5pBowZpnJUQBqMoKDi+EbzzzjspLCzkxIkTWCwWOnTowOLFi+3a1uTQzITTFH8SEdeTutgmdSlN\nanK9Y4eOcSu30o1u9LujH7tm7tI6JV2R8WKb1EUbaWlp/Pzzz3Tp0oXY2Fiys+1fAqrMOYJCCCHc\n0yXzJQbkDYApsPXoVjo37MzpL06T8noKQZODtE5PCHGDqlWroqoqmZmZVKlShbS0NLu3lUvDQggh\n/lL65nQShiRw655b8a7vXebrLAUWzsw7Q4PHGqCYZPFo4eacdGn4008/5eLFi3h5efHDDz9QtWpV\n1q1bZ1+KRmwE5WYRiSWWWGLnx18/+DW5Kbk8/MfDKIpi+/XfQa3DtYhYHaF5vhJLrHlcxs0i06ZN\nY8WKFRQUFPDss8/SvXt3YmJiMJlMhIeHM2vWrHLfBayqKoqisHfvXpo3b46fn5/dGxqKAVN2ivXr\n12udgi5JXWyTupQmNbHt2roU5RWp29ttV0/NPWXztflp+eqmupvUzH2ZTspOOzJebJO63ABK9S3r\n169XBw0apKqqqmZmZqqTJ09W77rrLnXDhg2qqqrqU089pX7//fflOszevXvVHj16qG3atFGnT5+u\nrlixwu5tTeVqN4UQQrgtk7eJ1gtbc+TFI+QczSn186NTj1Lv/npUDauqQXZCGMOaNWto27Yt99xz\nD4MGDeKuu+5i586d9OrVC4D+/fuX+wluY8aMYe7cudStW5cRI0YwZcoUu7eVm0VcRPGpaHE9qYtt\nUpfSpCa23ViXahHVaDKhCUkxSbT/rX3JPMCsxCzO/eccnfd31iBL55PxYpvU5a+dP3+e1NRUVq5c\nyZEjRxg0aNB1l46rVatGenp6uffbokULABo1aoS/v7/d20kjKIQQolyajG/ChRUXODHzBE2ebwLA\n2UVnafbPZnjV9tI4OyG0ZTabMc+fD0ePQu/esGHDdT+vU6cOrVu3xtPTk9DQUHx9fTl58mTJzzMy\nMggICCjXMWvVqsXnn39OVlYWS5YsKdf2cmnYRRRPUhXXk7rYJnUpTWpim626KB4KrRa04vi042Tt\nywIg6O0gGj3byMnZaUfGi21SF+tZ0anz5zPVbGaqjXr06NGDn3/+GYBTp06RnZ1N37592fC/hnH1\n6tUll4ntNWfOHFJSUqhTpw6xsbHMmTPH7m0NeUYwJiZG7hq+IS6ml3z0EsfFxekqH73ExfSSj8T6\njePi4mz+3C/Yj9PRp0m4J4HH9z2OyduEeaP2+Uqsz/HizvGNBg4cyO+//07nzp2xWCz861//IjAw\nkMcff5z8/HzCwsIYNmyYzW1vdPz48ZKvR48eXfJ1ZmYmtWrVsmsfhlw+xmApCyGES1JVlb137qV6\nx+oEvS4LTQthiyP7FpPJRGBgIPXr1y/1sy1btti1D2kEhRBCVFje6Txi28fS9se2+N9m/wR1IdyF\nI/uWZcuW8c0335CXl8ewYcMYMmQIVauW7659k0MyE05X1ilodyd1sU3qUprUxLY/q0tRVhE+DXxo\n8WkL9o/aT1F2kfMS05iMF9ukLs41ZMgQli5dyoIFC8jLy+OBBx4gOjq6ZA6iPaQRFEIIUW5Z+7PY\n0XYHlgIL9e6tR/Vbq3PkpSNapyWEWwoICOCxxx7j5ZdfJisri5iYGLu3lUvDQgghyi2+fzw1/16T\nJmOty8cUXCogNiKWlnNbUusO+yapC+EOHN237NmzhyVLlrBq1SoiIyMZMWIEffv2xdPTvvuB5a5h\niSWWWGKJyxW3zWlLzpEcLra5yGHzYaKiovCq6cX5586za8Qunjj4BF41vXSTr8QSaxk7UlhYGIqi\nMHz4cBYuXFjyfOEjR44QGhpq1z4Md0awulKdK5Yr5X4Ys6szm80lg09cJXWxTepSmtTEthvrYimw\nEBsRS/B7wdS5s06p1x989iBF6UW0XtTaiVk6n4wX26QupTnyjGBxrW31ROvXr7drH4Y7I9iTnqxa\ntoqBQwdqnYoQQridU5+fwqeJD7UH1rb585B3Q4iNjOXcd+eoN6yek7MTwr1UxllHw50RXK+s5+vm\nX3PE+wgjx4wk5skYrVMSQgi3kZWYheKpUCW0SpmvSd+aTsI9CXSK64TPLT5OzE4I/dH7vQ0mrRMo\nLwWFnNQcnnvxOaKfiNY6HSGEcCtVw6r+aRMIUKNLDRo+3pADjx3Q9R9AIVzVtc8u/iuGawQnMIE8\nSx5Hpx6lIK1A63R0wxmTUo1I6mKb1KU0qYltFa1Ls1ebkX8qn9NzTlduQjoh48U2qYu2fvvtN4YO\nHUqHDh3s3sZwjWAsscR8HUNGUAa7e+4m93iu1ikJIYS4gcnbROtFrUmZlELOkRyt0xHCZWVmZjJr\n1izCw8O57777GDp0KMeOHbN7e8PNEbz2WnvqjFTOf3eeyD8i5S5iIYTQodQPUkn7IY3269ujeMj7\ntHA/jpwj+Oyzz/Lbb78xePBgYmJiGDNmDKtXry7XPgx3RvBaTZ5vQsTPEdIECiGEA6iqypsvvsmh\ncYcq/Pi4xmMbgwKpH6ZWcnZCiE2bNtGpUye6dOlCcHBwhfZhuOVj4PoFpTft2gRov2Ck1nHx9/SS\nj17imTNn0r59e93ko5e4+Ht6yUcP8Y210TofPcTvvf4euz/cTUCrAJ6Z8UyF93fb/NvY1XkX+2rv\nwy/ITze/n4yXyo/j4uIYO3asbvLRQ+xIcXFx/PHHH8yePZvnn38ei8XC/v37ad3a/nU8DX1pWFxl\nNptLBp+4Supim9SlNKnJVfO/mM+ijxcRnBfMiMMj+KrZV6RUTbmpJbtOzz3NiY9P0HF7R0zepspN\nWAMyXmyTupTmrL7lypUrfPXVV8yePRtFUYiNjbVrO5dsBDMTMqkWXs1JGQkhhGtRVZUV/1nB0keW\n8ljOYyxsspAhM4YwcOjACk/FUVWVhLsTqNq2KsFvBVdyxkLolxYnsHbv3k1kZKRdrzXkpeE/U3Ch\ngPh+8TR7pRmNnmqkdTpCCGFIp788TX5+PvNbzSfnZA6KotzUfGxFUWj575bsaLeD2nfWpkbXGpWY\nrRDuqWvXrja/rygKmzdvtmsfLtcIetX2IvL3SPbcsYfCS4U0ndjULW4mkdPxtkldbJO6lCY1ud7F\nahcZOX8k1RpVI/tiNscO2b8cRVm863sTOiuUpFFJdIrrhEdVj0rIVBsyXmyTujjXkiVLbnofLtcI\nAvgF+xG5MZL4v8dTeKmQ4HeD3aIZFEKIyqAoCpN+nARY/7BX5rPd6w6tS9oPaRx+8TChs0Irbb9C\nuKPAwMCb3odLzhEsVnCxgPgB8dS6oxZBbwQ5ODMhhBD2KLhcQGxELC3/3ZJaf6+ldTpCOJTeb3J1\n6UYQoDCzkMILhfg283VgVkIIIcrj0rpLJMUk0Sm+E141vbRORwiH0XsjaPx7+P+CZzVPt2gCnbFe\nkRFJXWyTupTmzjW5ZL5Ebqrtx3U6qi41+9akzpA6HHrmkEP272juPF7+jNTFeFy+ERRCCFG29C3p\nJN6bSF5qntOPHTwtmIxdGZz7zzmnH1sIYeXyl4ZtUVWVoitFeNZwyXtlhBDCLpl7M9lz+x5azW9F\n7f61NcnhyvYr7B20l067O+HT0EeTHIRwJLk0rEPpm9KJ7RhLTkqO1qkIIYQmco7kEN8/nuYfNdes\nCQTw7+xPw6cacuCxA7r+YymEqzLkKbFrnzVc0Wf/tRjXgrheceS9ngdB2j+LsDKeZXgz9XDVWJ41\nbDsu/p5e8tFDfGNttM7HkXGPyB7suWMP+ffms/+W/dSnfpmvd8azY3u90otdXXbx3xf+S51BdTSv\nj4yXisfyrGHb77d65paXhoud/eosyeOTabuiLf63+lfKPrViNptLBp+4Supim9SlNHeqiaqqpP+R\nTkCPgL98rbPqkpWYRVzvODps7YBfiJ/Dj3ez3Gm8lIfUpTS9Xxp260YQIG1FGgcePUCb/7YhoOdf\nvykKIYRwjNSZqZz/7jyRGyJRPOQhAMI1SCNYyRxR0MsbL+Mb5ItvY9dfZkYIIfRKtajs6buHWv1q\n0fSlplqnI0Sl0HsjaNI6AT0I6Blg+CbQKHMRnE3qYpvUpTSpiW3OrItiUmg1vxWp76eSGZ/ptONW\nhIwX26QuxiONoBBCuDBVVUmZmmKYVRJ8m/kS/F4w+0fux5Jn0TodIVyeXBouQ/ExFEXmqQghjOvI\nP49w8ZeLtP+tPZ7+xlgoQlVVEgYnULV1VYKnBWudjhA3RS4NG9SJmSdIfi4Z1aLf/3lCCPFnUmek\nkrYsjYjVEYZpAsH6h7Plly05M/8M6X+ka52OEC5NGsEy3PLwLWTsyiDp4SQshfq/PCHzMmyTutgm\ndSnN1Wpyet5pTnx0gog1EXjX9a7wfrSqi3c9b0I/D2V/9H4KMws1yeHPuNp4qSxSF+ORRrAMXgFe\ntFvTjoJzBewbuo+i3CKtUxJCCLtkJWWR8nIK7da0w7eJcW+Eq3N3HQJ6BnD4hcNapyKEy5I5gn/B\nkm9h/6j9FJwrIPyHcDyrG+fyihDCfeWfz7+pM4F6UZheyI52Owj9LFTTR+EJUVEyR9DgTN4mwr4K\nI6B3AJYc/V8iFkIIwCWaQADPGp60mteKA48doOBCgdbpCOFypBG0g+KhEDglEO96+n1jlXkZtkld\nbJO6lCY1sU0PdanZpyb17qvHwWcOap1KCT3URY+kLsYjjaAQQgjdC3o7iKz4LM5+c1brVIRwKTJH\nUAghDCz/XD6J9yfS5r9t8KrlpXU6DpWxM4P4AfF02tUJn0Y+WqcjhF303rfo7ozg2bNnufXWW7VO\nwy5HXj5C+mZZ40oIoY3C9ELi+8VTo2cNl28CAap3rE6jZxqR9EiSrv+wCmEkumsE33vvPQIDA7VO\nwy41etUg4Z4ELv5yUetUZF5GGaQutkldSjNaTYpyith71178u/kT+Fqgw46jt7o0ndSUwkuFnPr8\nlKZ56K0ueiF1MR5dNYKfffYZDz30EL6+xlj3qna/2oR/H87+Ufs59+05rdMRQrgJS4GFxPsT8Wnk\nQ4uPW7jVozBNXiZaLWxFyqspZB/K1jodIQzP4XMEt23bxsSJE1m/fj0Wi4XRo0cTHx+Pj48Ps2fP\nJiQkhMmTJ3Po0CHOnTtHaGgov/32G2+//TZDhw4tnbAOr7Vn7skkfkA8ga8F0vCxhlqnI4RwcRdW\nX+DkpycJXx6OyUtXn+ed5sTHJzj3zTna/94ek6d71kAYgx77lms5tBGcPn06ixcvplq1amzevJll\ny5axcuVK5s6dy7Zt25g2bRrLly8vtd2oUaNYuHCh7YR1WtDsQ9kcnnCYNkvbYPKWNyUhhGOpFhXF\n5D5nAm+kWlT23LGHmrfXpNmkZlqnI0SZ9Nq3FHNox9K8eXOWLVtWUoBNmzbRr18/AG677TZiY2Nt\nbldWE6hnVVpUoe3ytpo1gTIvwzapi21Sl9KMVhNnNYF6rYtiUmg1rxUnPjxBRlyG04+v17poTepi\nPA59XtqQIUM4evRoSZyRkYG/v39J7OHhgcViwWQqX/MUExNTckNJQEAA7du3JyoqCrg6CN0tLqaX\nfPQSx8XF6SofvcTF9JKPxPqN4+LidJXPtfHWI1u5+NhFkkYm0WFHBzZu3air/Nwx1vN40SrWO4fP\nETx69CjDhw9ny5YtjB8/ni5dunDvvfcC0KRJE1JTU8u1P72fYhVCiMpWlF2ERxUPrdPQJVVV2Tds\nH34hfoRMD9E6HSFK0XvfYnLmwbp3786qVasA2Lp1KxEREc48vNOpFpXUGakUZRdpnYoQwqAu/HSB\nnZ13YimUZ53boigKoZ+HcnbxWS5vvKx1OkIYjkMvDRcrXtpg8ODB/Prrr3Tv3h2AefPmVWh/MTEx\nxMTEEBUVpZtTv7Zi1aJy+OfDHJ5/mB4be+BZw9Ohp571Xg8t4pkzZ8rUARtx8ff0ko8e4htro3U+\nAOaPzTAFOvzcAZOnSZN84uLiGDt2rD7q8Sdx6OehfH3f17Sc05K+A/o6/Hi6HC86iI0yXpwZ6508\nYs7BVItK8thk0jemE/FLBN71vB1yHLPZXDL4xFVSF9ukLqXprSYZcRnE/188rb9qTa07ammWh97q\n8meSHk1C8VBo+WVLhx/LSHVxJqlLaXrvW6QRdAJVVTn62lHOLTlHu1/b4dvUGAtmCyG0kX0om7je\ncTT/uDn1htXTOh3DKLxSSGy7WFp82oLaA2trnY4QgP77FmkEnejERydI35ROm2/baJ2KEELHzn51\nlqLsIho+LgvUl9flDZdJHJFIpz2d8K7jmCswQpSH3vsWQzaC0dHRhpgjaDNeZwYPmfPlrFjmCNqO\ni7+nl3z0EN9YG63z0UtsxDlfjVc2JvdoLuefOY+iKDJeZLxoGvfp00cawcqk985aK2azuWTwiauk\nLrZJXUqTmthmxLoU5Raxs+NOmr3cjPoP1nfIMYxYF2eQupSm975FGkEhhBAuJ2NXBvH94um4qyO+\njWVettBOWX3LuXPn6NixI+vWrcNkMhETE4PJZCI8PJxZs2aVrLjiaCanHEWUyVJo4dL6S1qnIYTQ\niFqkkn0oW+s0XE71DtVpNKYRBx4+gGqRkwdCXwoKCnjyySepWrUqqqry/PPP8/bbb/P777+jqio/\n/PCD03IxZCMYExNTcu3dbDZfN1fDaPHv3/3Onvv3cPJfJ29qf65Sj8qOZ86cqat89BIXf08v+egh\nvrE2Tjn+ejMb7trAkYlHNP/9y4pnzpypq3zKEx/peoStJ7dy6rNTlb7/4q/19PvqITbyeHF0fK0J\nEybw9NNP06BBAwB27dpFr169AOjfvz9r1661uZ1DqAZjwJT/UvaRbHVLyBY15Y0U1WKxVGgf69ev\nr9ykXITUxTapS2la1CR5QrIae1usWpBR4PRj28voYyXrQJa6qc4mNSspq1L3a/S6OIrUpbQb+5Z5\n8+apb775pqqqqhoVFaXu379fbdiwYcnP161bpz700ENOy0/mCOpE3uk84v8eT807ahLyfojT5gYI\nIbRx/N3jnFl4hsjfI/Gq7aV1Oi7t5KyT1lr/EYnJ05AXwoSB3Hgm8LXXXruub+nduzeKoqAoCnFx\ncYSGhrJ7927y8/MB+OGHH1i7di2ffPKJU/KVRlBHCi4WsHfgXureW5cmzzfROh0hhIOc+vcpjr99\nnMhNkfg08tE6HZenWlTi+8VTo1cNAl8J1Dod4Wb+rG/p06cPn3/+ORMmTGD8+PH07t2bp556ir59\n+3Lvvfc6JT/5aKQjXrW8aLe2HQ0eb1Dubcuah+DupC62SV1Kc2ZNPGt4ErEmwhBNoCuMFcWk0HJu\nS05+fJKMXRmVsk9XqIsjSF3KT1EUPvjgA6ZMmUK3bt0oLCxk2LBhTju+p9OOVIliYmKMu6D0X8Qb\nd2ys0PbFtM5fb3FcXJyu8tFLXEwv+bhdfJ/O8vmLBYL1lE9FY9/Gvpx9/CyLhyzmiaQn8PD10FV+\nrhK7ynipzPjPrF+/vuRre17vCHJpWAghhFtQVZXE+xLxaeZD8/eba52OcBN671tMWicNLRysAAAg\nAElEQVQg/polz0L+2Xyt0xBCCENTFIUWn7Xg3JJzXN5wWet0hNAFaQQN4MJPF9jVbRc5R3LKfI1W\np5T1Tupim9SlNEfVJCclh/Qt6Q7ZtzO42ljxruNNyy9bkhSTROGVwgrvx9XqUlmkLsYjjaAB1B1S\nlyYvNGF3r91kJmRqnY4Qwk55Z/LYc8ceMuPk362e1B5Ym5p31CR5XLLWqQihOblZxCjx01F4BngS\n2ysW3oSo0TrLT6dx8ff0ko/E+o2jKvn9pOByAVu6b4He0OjpRpr/fjcTF9NLPpURh3wQwpehX3Lg\nrQPc/c+7y719ZY8XV4qL6SUfrWO9k5tFDObCTxdIejiJtqva4t/JX+t0hBA2FGUVsef/9lD91uo0\n/7C5LBCvU5c3Xibx/kQ67emEd11vrdMRLkrvfYtJ6wRE+dQeWJu2q9pStU3V675vlE8eziZ1sU3q\nUlpl1URVVRKHJ+LX3I/mM4zfBLryWAnoGUD9h+pz8MmD5f5D7cp1uRlSF+ORRtCA/Dv54+HnURKr\nqsqSL5fo+hOHEO5CURSaTmxKyzktUUzGbgLdQeDrgeQcyuHs4rNapyKEJuTSsAtY+d1KFj+ymJHz\nRjJw6ECt0xFCCEPJiMsg/v/i6RjbEd+mvlqnI1yM3vsWOSNoYPO/mE/fNn354eUfeDLjSb5/4Xv6\ntunL/C/ma52aEEIYRvX21Wk8rjFJDyehWvT7B1sIR5BG0MCin4hm7NSxFOUWoaCQcyyHYdWHMbjd\nYK1T0w2Zr2Kb1KU0qYlt7lKXJhOaYMmxcPLTk3a93l3qUl5SF+MxZCMYExNTMtjMZvN1A8+dYkVR\n2J+4n4wLGcxqNovCaoVk1Mpg9z272d1rN2kr0zD/pp98tYjj4uJ0lY/ELhi/bObS+kv6yaeS4+Ln\ndeslH0fFJk8T50af4/tXvycrKUvzfIwau8t4qUisVzJH0OBmTZtFYGggA4YMYNWyVRw7dIynXniK\n89+d58QHJwhbGoZfkJ/WaQrhktJ+SOPgUwdpt74dVVtV/esNhO6d/OwkZ+aeIXJzJCYvQ54rETqj\n975FGkEhhKiAS+ZLJN6XSMTqCKp3rK51OqKSqKpKfP94anSrQeDkQK3TES5A732LfNxxEeU5/Zxz\nNIfcE7mOS0ZHjHBaXgtSl9LKU5OMnRkk3pdI2NIwl28C3W2sKIpCqzmtODnrJFdir5T5Oneri72k\nLsYjjaAbSt+YTmxELPtj9suzi4Uop6LcIhKGJtDy3y2pGVVT63SEA/g08qH5R81JGplEUU6R1ukI\n4VByadhNFVws4NTnpzj5yUmqdahGkwlNCOgdYPinIAjhDPnn8vGu5611GsLB9j2wD58GPjT/sLnW\nqQgD03vfIo2gmyvKLeLsorOc+uwU7da1w6uml9YpCSGELhRcKGBHux20XtSamn1qcsl8icvmyxx7\n7RgAzaY0AyAgKkDODovrXDtW+tBH132LXBp2ERWdl+Hh60HDxxvSaVcnl2wCZb6KbVKX0qQmtrlz\nXbxqe9Hy3y1JikmiML2QmlE1CZoaBEAccQRNDSJoapA0gddw5/FyrWvHit5JIyj+VPaBbPLT8rVO\nQwghNFG7f21q9a9F8thkrVMRwiEM2QjKgtKl46ioKIfsf/vH29kcvJmDzx4k50iObn5fe+Pi7+kl\nH73EjhovRo6joqJK/3ydGXMfM1e2XdE8P63ia+khHy3ikPdDuLzxMsvfXF7y8/a0101+eoqvpYd8\ntI7juLrAtl7JHEHxl/JO53Hyk5Oc+vIUNfvWpMmEJvh38tc6LSEcSrWoJD2cRMH5AsJ/CJfFhd1c\n+h/p7Bu2j057OrG5/mYAotQobZMSumdWzDJHUDjHjZ/EKpNPAx+C3w6mS0oX/Lv4c2j0ISz5Focd\nrzI5si5GJnUp7dqaqKrK4fGHyTmcQ5vv2rh1EyhjxapG9xrcEnMLB544gIrKB3yg6z/uWpHxYjzu\n++4mys2zuidNxjWh4/aOmLxl6AjXdeytY1z67RJtV7bFo4qH1ukInQicGkhuSi472MEVrrBq2Sqt\nUxLipsmlYVFpMhMy8W3qi6e/p9apCFFhuSdy2TtwLxG/ROBzi4/W6Qgdmf/FfBZOX0jDIw15lEf5\nusXXHPE6wsgxI4l5Mkbr9IQOyaVh4VbOfX2OrUFbOTzxMHmn8rRORwi7qarKWxPfQlVVfBv70mlX\nJ2kCRSnRT0Qz7p1xqKgoKORfymfca+OIfiJa69SEqDBpBF2EHuZlBL8dTMfYjliyLOxos4OkR5PI\n2p+laU56qIseSV2u99N/fyLuk7iSS32Khzxhp5iMlasURUFRFPLJZwpTyLqQxfmvzqMW6fdsj7PJ\neDEeaQRFpfIL8qPFJy3ofKgzvs18Ofj0QV2fEhfubf4X8+nbpi8/vPwDo7NHs3zScvq26cv8L+Zr\nnZrQqWOHjnErt3IP9zByzkiO7D1CfL948s/LeqvCmGSOoHAoVVXl+cVClzITMkn9MJWteVvZ+PtG\nRqWOYmGThQyZMYSBQwfKuBVlMitmwLp8jKXQwtFXj3L267O0+bYN/p1laS1xlcwRFG6vrD+mGXEZ\nFOUWOTkb4e5Ui8qFVRfYc8ce4v8vHr8gP2r+rSbZl7OZHzafrMtZJZf/hLCHydNE8LRgms9szt47\n93Lqy1O6/qMvxI2kEXQRRpuXcfLTk2wN3MrRN49ScLHAYccxWl2cxR3rUnilkO1h20l5JYX6o+rT\n5WgXAl8J5OTZk4ycN5LoT6MZNW8Uxw4d0zpVXXHHsWKPG58YUXdwXSI3RXLioxMcePQARTnu+UFX\nxovxyDofQhOtZrcia18Wqe+nsq35NuqPrE/jcY3xC/TTOjXhojz9PQlbEka19tWuO+P3zKRnAOsf\nsIFDB2qVnnABVUKr0GFbBw48doDdPXbT5r9t5D1N6J4hzwjKs4ZLx0Z8dmzVNlU5E32Gwi8KMfmY\nODzucKUfr/h7evh99RQbcbyUJy5+8s2NP9+ZvpMNGzbY3N7ms4Yl5lp6yEcvcVnPGt4Uu4mwJWHU\nH1mf2ZGz+XH6j7rI11nxtfSQj9axPGvYAeRmEdclN5aIm2EptJD2fRonPjxBQFQAwW8Ha52ScFHX\n3izyZy7/fpnEBxJpOLohzV5uhmKS9zd3IzeLCKe58ZOYEZV5Y8muDCwFFXu2sSvUxRFcqS4Flws4\n/v5xtoVs4+QnJ2nyQhOC3ggq935cqSaVSepimz1negJ6BdAxtiMXV18k4e4ECi47bj60Xsh4MR5p\nBIWuqarK0SlH2dZ8Gyc+OkFhZqHWKQkdKcwsZHvL7WTtySJ8WTiRv0dSd0hdWRBa6IZPQx/ar2+P\nb7AvOzvtJDM+U+uUhBOp6PdMYDG5NCwM4cqOK6S+l8rl9Zdp8EQDGv2jkTwCTABQmF6IZw257004\n3iXzJS6bL5f6fkBUADWjav7l9me/Okvy2GRCPgzhloducUSKQmemK9N5iZd03bdIIygMJedwDqkz\nUrHkWGg1t5XW6QgnKcotovByoTT/wvAy4zPZN3QftfrVIuSDEEzecmHOFc3/Yj6LZi6iQVIDvuIr\nXfctMgJdhLvMy/AL8SN0Vigt57S06/XuUpfyMkpd8s/mkzI1ha2BWzkz54xDj2WUmjib1MW2ital\nWkQ1OuzoQO6xXOL6xJF3Kq9yE9OYjBfrlKb+tfoz4PwAQ1walkZQGFJZN5Zcib2CarH+w1NVlSVf\nLtH1JzFhW+aeTJIeTmJ7q+3kn8mn/W/tafbPZlqnJUSl8ArwInx5OLUH1GZnp51c3lD6crMwpuwD\n2cT/PZ7jrx+n8ZjG5KP/Z1DLpWHhMiwFFnb33E1RehFNXmhCbJVYvnryK0bOGykLBRtIUXYROzvu\npP7I+jR8siFetb20TkkIh7n4y0X2R++n6UtNaTy2sSyhZVBFWUUce+sYp748RbN/NqPRs4347P3P\nyHo5S+YIVjZpBMWfUVWVz8d/zldffkVQbhCPFD3C4oaLORpwlJFjRhLzZIzWKQo7yJqSwp3kHM1h\n37B9+IX40XJOSzyryc1PRqGqKmnfp5E8LpkaPWoQ8l4IPg2vzmWWdQSF08i8DCtFUXjqg6d4ad5L\neNb1REGhMKeQca+NI/qJaK3T0w09jJfcY7lk7rG9lIYWTaAeaqJHUhfbKrMufoF+RG6KxKOaB7tu\n20X2gexK27ezudN4yT6UTXz/eFJeTaHVglaEfRV2XRNoFNIICpejKAqKopCTlcOsZrPILcwt+d61\nTs46ScLgBFJnpHJlxxUshRVbtFrYT1VV0jens+++fcRGxpK+OV3rlITQBQ9fD1rNaUXjsY3Z3WM3\n55ed1zolUYai7CJSXk1hV9dd1LqjFp3iOtm1fJBeyaVh4ZJmTZtFYGggA4YMYNWyVRw7dIzRE0df\n95q8M3lcXn+Z9I3ppG9MJ/dYLv63+RP4WiA1utXQJnEXZSm0cP6785z48AQFaQU0fq4xt8Tcgqe/\nXAIT4kZXdlxh37B91Btej6A3gzB5yjkbPVBVlQs/XiB5bDLVb6tO8w+a49Poz88AGuHSsDSCQvxP\nwcUC0v9Ip2p4VfyC/Er93JJnweQjb8gVYcm3kDgikfoP1afOoDry5A8h/kL++XwShyeCCmHfhOFd\n11vrlNxazuEcDo05RO6R3P9v787joqr6P4B/LovIvikugKBAKYiAipqkDpmKaSkoJOEySpm7qZXL\nrzS1NH0s91zIBxc0H1dKcy9HMRUR0h7BR0FZJTVFWQRBmPP7AxkY5qIgzNw7zPf9et1XnZkzd77z\nnTOX4zn3ngu3dW6w7lu7EUBt6AjSX7VGQpfOy6iLuuTF0MYQzd5txtsJBICENxIQ5xmHm5Nu4t5P\n9/A062kDRal5mm4vek300HFfRzQfKt7bv9FviB/lhZ+689KkeRN4HfeCRXcLxHeNR96lPLW+X0Np\nbO2lrKgMqQtSEd89HlZ9rND1atdadwK1hag6glevXkXv3r0xduzYRteYiPbrHNsZr//7dRi7GuOf\nPf8g3iceF9teRNmTMqFDEwXGGHJO5eDhkYdCh0JIo8Dpc2i3pB1cV7viv4P/i+zN2aIeWWpsHhx+\ngDiPOBReL0TXP7uizedtGuWdYEQ1NRwREYFTp07BwsICS5YsQfPmzVXq0NQwEQvGGIpuFcHE1UTl\nOXmJHAV/FcDM26zRn99TVlSG+7vuI2tVFhhjaLu4LZoHqv52CSGvrvBmIa4FXYNFNwu4rXeDvrG+\n0CE1WkWpRUiZnoLCG4VwW+cGm342r7wvbZgaFlVH8Pr163BwcMDdu3fx448/YtmyZSp1qCNItEFR\nahH+++5/UZxRDIseFrDsZQnLXpaw6G7RaA7g8mJ5+QKqm7Jh3tUcDjMcYN3Xmtb/I0RNSgtKcePD\nGyi6WQSP/R41nsZCXk3Z0zJkLs9E1posOM5yhONMx3qfF64NHUG1D1XExsbC398fACCXyzFhwgT0\n7NkT/v7+uHXrFgBg/vz5CA0NxZUrV1BWVgYrKyuUlpaqO7RGhabS+QmVF+O2xuh2rRt6pPaA/RR7\nlOWV4fac27g+6rog8VTXEHnhmnDgDDj4nPVBp187weZtG63uBNJviB/lhZ8QeTEwM4D7T+5oMboF\nEnok4OEx8Z2Goa3t5eGRh4jrGIeCqwXomtAVTnOddObiQLWu3bB8+XJERUXBzMwMABAdHY2SkhKc\nP38esbGxmDVrFqKjo7Fo0SIAwIULFzB16lQYGhpiwYIF6gyNEI0wtDVEs/eaodl7zQCgxn8V5l7M\nxdPbT2HZyxJNHZtqMsRXxnEcnOc7Cx0GITqF4zg4fuII8y7mSBqRhNYTWsPp/5zA6WnvP8KEVJRW\nhJRPUlCYWD4NbBtgK3RIGqfWqeEDBw6gU6dOGDVqFC5cuICZM2eiR48eCAkJAQA4ODggKyurTvuk\nqWHSGOWcyEH2hmzknsuFnqkerHpZwbKXJWwH2b50nSp1Ks0vxd3Iu+AMONhPshcsDkKIquLsYiSG\nJMLQ2hDtd7SHoRXdl7u25MVyZK7IRObKTDjOcITjp/WfBuajDVPDah0RDAoKQlpamqKcn58PCwsL\nRVlfXx9yuRx6enVLvlQqhbOzMwDAysoK3t7ekEgkACqHpalMZa0q95fApr8NZKdlQAZgWWyJ3Jhc\n3My9CfhqPp7uzt1xZ+0dZP2YBfgAnZd3Fle+qExlKuPCzQuQfyWH4yFHxHeNx6O5j2DsYiya+MRa\n7lTcCclTk5Fkl4TW61rDaYST2t7vCq5A7NR+sUhaWhpCQ0Nx4cIFzJo1Cz169EBwcDAAwNHREZmZ\nmXXaH40I8pPJZIrGRyo11rzc+uwW9M31Ky9AManbBSg15aVi4efHssdoNa4V7Cfbo6mTdkxV11dj\nbSv1RXnhJ7a83Nt1DynTU+Cy0gUtR7bU6Hs/kj3CY9ljAMDFtIvo4dwDAGAlsRLVrdeeZjxFyowU\nFFwpgNsaN9gOUv80sM6PCFbn5+eHQ4cOITg4GBcvXkSnTp00+faENBpWfa3w+PfHSJ2XWr5MTScz\nWPayhNN8JxiYvfhnzRjDT5t/Qp8+fVQu7tBroocWH7RA+63tX7ofQoh4tPigBUw9TZEYlIj82Hy4\nfOeisTXvrCXWig5fuiwdbSVtNfK+tSUvkSPz+0xkrsiEwzQHdNjZAfpNhVu94dmzZxg3bhzS09NR\nXFyML774Ah06dIBUKoWenh46duyI9evXa+ziO40c6Ss+TGBgIE6ePAk/Pz8AQGRk5CvtTyqVQiqV\nQiKRiGaomcriLFc8JpZ4GqwcIIFtgG15+SnQ1qgt8s7n4VzsOUD/xa+/dOYScg/n4siBIzC1NVXd\nvw0gMRPZ59VAWULHkxrLFcQSjxjKYmwvlx9eRunKUphsNsEVyRU8mPEATZo30en2knc5Dy1+bAGT\n10zwZM0TpLVOg3NTZ429P9/U8M6dO9G8eXPs2LEDjx49gpeXF3x8fLBkyRL07t0bEydOxM8//4yh\nQ4eqvFYdRLWOYG3Q1DAhdVOUWoSrb12FrIUMRzOOwlXfFSOzRmKb1TZkts7EqGmjIP1YKnSYhJAG\nwuQMGUszcGf9Hbj/5A6rPlZCh6RxT7Oe4tbMW8i/nA/X1a5o9m4zQeLgmxp+8uQJGGMwMzPDw4cP\n0a1bN5SUlChOlfvll19w4sQJrFu3TiMx6mnkXYjaVf+XGClHeQGaOjeF5xFPjBo7CqGuoSi+VwwO\nHDgTDjMWzsCY8WOEDlEUqK3wo7zwE3NeOD0OTv/nhPZb2yPx/URkfp+psQEUofMiL5EjY3kGLntf\nhkkHE/gm+grWCayJqakpzMzMkJ+fj+DgYHz99deQy+WK583MzJCbm6uxeOgkIEIaOY7jYNrBFKYd\nTOFg64DSK6VY33o99HL0wHGcVi8CTQipmU1/G3SJ7YJrw64hLzYPr295vVGf+/vo90dInpyMpu2a\noktsFxi7CHPnFZlMpugQpyGNt05mZiaCgoIwefJkhIaG4vPPP1c8l5+fDysrzY3iauWIoFQqVSS5\nasJ1uVz1nAQxxCOWcsVjYolH8PJxGTp/2hl7UvdgdORoyI6LLD4ByxXnfIklHrGUqxJDPGIpa0t7\nuZh6ET7nfKBvro8Ijwgc235Mre9XlaY+b/GdYiSFJmHXB7vwd9jf8DzsCWMXY8HyL5FI8NVXX0Ei\nkcAb3qju3r176N+/P5YvXw6pVAoA8PHxwZkzZwAAR48eRe/evVVepy50jiAhhBCiA7J/zEbq3FS8\ntuk1NA9qLnQ49SZ/JsedNXeQvjQd9hPt0WZumzovpaVufOcITp8+HXv37sXrr7+ueGz16tWYNm0a\nSkpK4O7ujoiICI3N1lBHsJGo+FcIUUZ54Ud5UUU54Ud54aetecmLy0NicCLsRtih7ddtoWfQsBOD\nmsrLI1n5NLCRoxHc1rrBxM1E7e/5KrRhHUGtnBomhBBCSN1Z+Fqgy+UuKIgvwF8D/kLJ/RKhQ6qT\n4r+LkRSWhP+N+R/aft0WnY52Em0nUFtoZUeQzhFULdM5gvzlisfEEo9YytReVMvacs6XpstViSEe\nsZS1ub00adYEnY51QlKrJER0jEBebF6D7b+qhoxfXirHnsl7sLn9ZjR1bopuSd2QaJ2oOK+uod+v\nocp0izk1oKlhQgghpGE8+PkBbnx0A20Xt0Wr8a1EuYrA45jHSJ6cjCYtm5RPA7+uPSOANDVMNKb6\nv8RIOcoLP8qLKsoJP8oLv8aSl2ZDmsHnnA+y1mbhxrgbKCsqq9f+GjIvxXeLcX30dVwPuw6n+U7o\ndLyTVnUCtQV1BAkhhBAdZvKaCbrEdoH8qRx/+v2JotQiQeORl8qRtSYLlz0vo0nrJvBN8oXdcDtR\njlY2BjQ1TAghhBAwxsqXY1mSjvbb2sM2wFbjMeT+kYubk27CsJkh3Na5wbSDqcZjaEg0NawmdLEI\nlalMZSpTmcoNWz5z5gxSvFLgsc8DN8JvYPe43Tj9+2mNvH/J/RJsC9iGqKFRcPo/J3id8kLcvThR\n5edVynSxiBrQiCA/mUw717RSN8oLP8qLKsoJP8oLv8ael+K/i5EUkgR9S3102NEBhtaGtXpdXfPC\nyhiyN2YjbWEaWo5pCaf5TjAwbzy3waMRQUIIIYRoHaNWRvD63QvGrsaI7xqPgqsFDf4euRdyEe8b\nj3/2/QPv095w+ZdLo+oEagsaESSEEEJIje79dA8p01LgstIFLUe2rPf+Sv4pwe05t5FzLAcuK1xg\nN6LxXghCI4KEEEII0WotQlvA63cvpC9Mx80pNyEvkb/SflgZw50NdxDnEQcDKwN0u94NLUJbNNpO\noLbQyjFYqVQKqVSqWNkdgNKdEnSxXPGYWOIRS3nVqlXw9vYWTTxiKVc8JpZ4xFCunhuh4xFL+cqV\nK/jkk09EE49YyrrWXsw8zVCwsgCJSxNRICmAx14PXEi+oFK/pvaSdykPP436CXpN9RD6eyjMOpqJ\n6vOpq0wXi6gBTQ3zk8lkisZHKlFe+FFeVFFO+FFe+OlqXpicIePbDNxZdwcddnWAtcRa6fnqeSl5\nUILUeal4ePgh2i1vhxZhujUCqA1Tw9QRJIQQQkid5JzIwfXR19HmszZwmOmg0rljcoa/f/wbqV+m\nwi7UDm0XtoWBpVZOQtaLNnQE9YQOgBBCCCHaxaa/DbrEdsH93feR9H4SSvNLwRjDN3O+QW5cLhJ6\nJODu9rvwOuEFt1VuOtkJ1BbUEWwkqp6vQipRXvhRXlRRTvhRXvhRXoCmTk3hHeMNA0sDJHRPwN4l\ne3FyxUls6bcF9lPs4RPjAzMvM6HDJC9BHUFCCCGEvBL9pvq40PUCPnv8GX758hcMKRuC682uI2xZ\nGLZt3iZ0eKQW6BxBQgghhLwyxhgO7zuMg7MOYnTmaGx33I6g74MwaNggnbowhA+dI6gmdK9hKlOZ\nylSmMpXFUeY4DhzHIe1BGr51+hZPHj8Bx3E4c+aMKOITskzLx6gBjQjyk8l0cymDl6G88KO8qKKc\n8KO88KO8KFu/dD2cX3OGiY0JCnMKkZ6cjklzJgkdluC0YUSQLuMhhBBCSL1MnjsZQHkHedCwQQJH\nQ+qCRgQJIYQQQtRAG0YEtfIcQUIIIYQQUn/UEWwkqp6gSipRXvhRXlRRTvhRXvhRXvhRXrQPdQQJ\nIYQQQnQUnSNICCGEEKIGdI4gIYQQQggRLa1cPkYqlUIqlUIikSjOR6hYz0lXyxWPiSUesZRXrVoF\nb29v0cQjlnLFY2KJRwzl6rkROh6xlK9cuYJPPvlENPGIpUzthdpLrfJBC0o3PJoa5ieTyRSNj1Si\nvPCjvKiinPCjvPCjvPCjvCjThqlh6ggSQgghhKiBNnQEtXJquC6ysrIQGBiIhIQEyOVyocMhhJBa\n4TgOdnZ2CA8Px/z582FkZCR0SISQRkhP6ADULTAwEEFBQSgqKgJjjDbaaKNNK7aSkhKcP38eV69e\nRe/evVFUVCTIMbTquXCkEuWFH+VF+zT6EcGEhAT88ccfaNKkidChEEJIrRkYGKBdu3bYu3cvzM3N\nER0djeHDh8PQ0FDo0AghjUij7wjK5XLVTqBMVr4tXFheXrCg/L8SSflWGw2xDwKAvg5d9Uj2CI9l\nj5G+MB0A4LTACQBgJbGCtcRa7a/XFsbGxigrK8P9+/fx4MEDtGrVSqPvTyf+86O88KO8aJ9Gf7HI\nC+tzXPl/65OChtgHAUBfh66ScTIAgIRJBHm9NuA4DqtXr8bQoUPRpk0bocMhhNSSNlws0ujPEdQG\nz549Q+vWrTFw4EDFYzKZDJ6engCAuLg4TJw48aX72bp1K/T09LCgYjjsOcYY2rVrp9jfpk2bsGzZ\nsgb8BI1DWVkZvv/+e/j6+sLHxwceHh6YM2cOSkpKAJSvX/ndd9+pPY7Bgwdj27ZtKo9v3boVlpaW\n8PHxUdoOHz6s9pgaq7S0NOjp6aFPnz4qz40dOxZ6enqIj49/aZ2cnBwAwMWLF/HWW2/By8sLnp6e\neOedd5CUlKSor6enh06dOql8hxkZGer7kPVE53zxo7zwo7xon0Y/NfwiDMC/AHzGGLiKoSQB9nHw\n4EF4eXkhISEB//vf/9C+fXul5xMTE5GVlfXS/XAchzZt2mDnzp1YWDFHCiAmJgZFRUUwMzMDAHz8\n8cevFKf6lWeTsc9eOZf12cfEiRORm5uL33//Hebm5igsLERYWBg+/PBDbN++HRzH1SOu2nvR+/Tp\n0we//PKL2mPQJAaG3diNPqzPK+W3vq9v2rQpkpOTkZGRoRhte/LkCc6dO6fYn5GR0UvrFBcXY/Dg\nwTh16hS8vb0BADt37sTAgQORlpamqCeTyWBjY1PnOAkhRB10ekTwOIC/AZw4cEDQffzwww8IDAxE\nSEgIVq1apfRcVlYW5s+fj5iYGISHh790X56enjA3N8eFCxcUj23btg0jR45UDKCvr2sAABdCSURB\nVE1/9dVXmDp1KgDA2dkZCxcuRO/eveHs7IzZs2e/8ueov/JsHjhwQuP7SE1Nxa5du7BlyxaYm5sD\nAExMTLBx40YEBQUp6lXkMCYmBm+88Qa8vLzg6+uL48ePAwDu3r2L/v37o0uXLujSpQvmz5+veO2W\nLVvQtWtXdO7cGf369cONGzcAANnZ2ejXrx86duyIgQMH4u7duzXG+aLphcWLF8PDwwNeXl4IDg7G\nvXv3AJSfszNs2DB4eHhg3bp1kEgk+PTTT9G5c2c4ODjgX//6Fz799FP4+vrC3d0d165dq1Pu6isO\ncchBDo4cOCLI6/X19fH+++9j586discOHDiAoUOHKvJtYGDw0jqFhYXIzc1Ffn6+ok5YWBjWr1+P\n0tJSxWNiniLiQ+d88aO88KO8aB+d7AhGbdqEwR4eiAHwPYCzc+disIcHojZt0ug+ACApKQmxsbEI\nCQnBmDFjsGPHDsU0EwA4ODhg8eLF6NWrF7Zs2VKrfY4aNQo7duwAUP7H6dy5cwgICFCqUzE6wXEc\nnjx5grNnz+L8+fNYu3Yt0tPT6/QZ6mvTpih4eAwGnmdz7tyz8PAYjE2bojS2j4SEBHh4eChGTSu0\naNECQ4cOVZQ5jsPDhw8RHByMNWvW4OrVq4qOdlpaGiIiIuDi4oL4+HjExMQgOTkZeXl5OHPmDLZv\n346YmBgkJCTgs88+U3QwJ0+ejJ49e+LatWv44YcfFB1EPjExMUpTipMmTQIAREZG4tixY7h8+TKu\nXr2Kjh07QiqVKmK2sbFBYmIipkyZAo7jkJ6ejoSEBBw4cACzZ8+Gv78/4uLiEBAQgLVr19Y67/Wx\nddNW9PXoi7/wFyZhEqLnRqOvR19s3bRVI6+vatSoUYiKqmwr27dvV+SvtnWsra2xfPlyBAQEwMXF\nBaNHj0ZkZCT69u2rdKWvv7+/0nc4bNiwOsdLCCENRSunhutyr2E+YePHw9bGBmdDQsABkCcnYwqA\nARMmABMm1CqGMAC2AM4C5ft4+hRTlizBgDoe1Dds2IBBgwbBysoKXbt2Rdu2bbFp0yb07NlTUae2\nIwgV9cLCwuDl5YU1a9bg4MGDGDJkCAwMav6qhwwZAgBo3bo17OzskJOTAycnpzp9jvoYPz4MNja2\nCAkpz2ZyshzAFEyYMKC2XweqfyNPn8qxZMkUDBs2oFav1tfXr9WC44wxxMbGwtXVFb6+vgAAd3d3\n+Pn5QSaTYeDAgXjnnXeQkZGBt99+G99++y0sLCzw66+/IiUlRel7ffToER49eoTffvsN33//PQCg\nbdu26NevX43v36tXLxw6dEjl8WPHjmHcuHEwNjYGAEybNg3ffPMNnj17pnhdVRWd0Hbt2gGA4h8K\nLi4uGjvHZ8z4MbC1scXukN3gwKEwuRADMABOE5wgm/DyGJzghP7oj7/wFzhwKHtahhlLZmDQsEF1\njqVz587Q09NDQkICmjdvjvz8fHh4eNS5zowZMzB+/HjIZDKcPXsWy5Ytw7Jly3Dp0iVYWFgAqN/U\n8KVLl3D79m26d6wIylV/J2KIRyxlai/ad69hMC1T15Brqn907172CcBmAGy6uTk7tm9fnWOp7z4K\nCgqYhYUFa9myJXN2dmbOzs7MxsaG2dvbs5MnT7KOHTsyxhiLjIxkgwcPfun+qtYbOHAg+/nnn1n/\n/v1ZYmIiO336tGJ/CxYsYFOnTmWMMebs7Mzi4+MV+6he1pS9e48y4BMGzGDm5tPZvn3HNLqPrKws\nZmpqyvLz81UeHzRoECsqKmJSqZStWLGCHT58mPn5+SnVGzx4MIuIiGCMlX+v0dHRbNq0aczOzo6d\nP3+ezZo1i82ePVtRXy6Xs/T0dCaXy5mFhQVLSUlRPBcSEsK2bdumEuOL2sHw4cMV788YYw8ePGAc\nx7Hi4mImkUjY/v37Fc9VLf/zzz+M4zjFc2vXrmXDhw9/ab4ayqG9h9hwDGfDMZyFmIeww/sOa/T1\nqampzMzMjDHG2LJly9iMGTPYkiVL2Pr16xljjHEcxy5fvvzSOg8fPmQxMTFs+fLlSvsvLS1lHTp0\nUOS7om5dAWCrV69m6enpdX5tfZ0+fVrj76kNKC/8KC/KTuN0nfstmqaTU8MAkJmcjAAA3wEYGBmJ\nzORkje9j586dsLOzQ3Z2NlJTU5Gamorbt2+joKAA9+/fV9QzMDBQjOzU1ujRo7FixQrk5eXB3d1d\n5XkmsvOUkpMzgefZjIwc+LysuX3Y29sjLCwM48aNU5zjlZeXh0mTJqFZs2Zo2rQp2PMLgnr06IEb\nN24gLi4OQPnFPDExMZBIJJgzZw4WL16MIUOGYNWqVfDw8EBycjL69++Pn376SXH+X0REBPr37w+O\n4xAQEIDNmzcDKD8n9LfffqvzZx8wYAAiIyNRWFgIAFizZg369OmjWEOz+vctlu8/PTkdvvDFJEzC\n6MjRSE+u22kJ9X19VSNHjsSePXvwn//8Bx988EGd69jZ2eGbb77B2bNnFY/duXMHT548UVyxD4gn\n97VF53zxo7zwo7xoH62cGm4IH82dC8ybBwB1ns5tqH1s3LgRM2fOVLrS0dLSEtOmTcOqVasUj/fs\n2RNffPEFhg0bhv3792PQoEGYOHEiBg8erLS/qlebDhkyBBMmTMCSJUuUnq9e70Vqeh91mDv3o4pU\n1no6t6H38cMPP2Dx4sXo2bMnDAwMUFxcjMDAQMUV2BU5s7W1xd69ezF16lQUFhZCT08PW7duhaur\nK2bMmIExY8bA09MTRkZG8Pb2RmhoKAwNDTF79mz069cPenp6sLS0xMGDBwEA69evx9ixY+Hu7g4H\nBwd4eXnxxvei7y08PByZmZno1q0b5HI53NzclC5sqP66quXq/6+JK6MrTJ47GbJ5MgB4pSnd+r4e\nqPz8rVu3hru7O6ysrGBlZaX0XG3qvPbaa4iOjsaXX36JjIwMmJiYwNLSEhEREXBzc1O8n7+/P/T1\n9ZViWLp0KQICAjT6myOEEIAWlC7/L61gLAr0degmWlD65YRcUFomk9EoDw/KCz/KizJaUJoQQggh\nhIiWbo4IVtyYtrpXubltffZBANDXoasq7hVcXV3vNfyqr9cmdIs5QrSTNowI6mZHkBBCtAh1BAnR\nTtrQEWz0U8Mcxymt6k8IIdqkpKQEenrCHaplfEPthPJSA8qL9mn0HUE7OztR39CdEEJe5PLly2jZ\nsqXQYRBCGqlG3xEMDw/H9OnTUVRUJHQohBBSayUlJTh//jyGDh2quBNM9WVnNIGuAOVHeeFHedE+\nWtcRtELdFmSdP38+CgoKYG5urlgjrTFu1iKIQYwb5YXyoq05MTY2RnBwMN5//3107NgRRkZGsLbW\n7EUwjDEsnzNH1Oc3CYHywo/yooqBwUroIF5C6xaUDgJw4sCBWi/gbGRkhNOnTyMxMRG//fZbo2yg\nyVeuoGzXLhh88AFcvb2FDkc0KC/8KC+qxJ4TfX19DB06FCYmJhp93+P79yNz7Vqc8PV95YX3GyPK\nCz/Ki6oEnEWQ0EG8hNZdNcw4Dl+4uOCqoSFGTJyIkePGlT/RtClgwNOvffIEeH6xSFlpKYqLi8s7\ng6amgKGhav38fKDidm5VU2NuDjy/XZeS3FygpES1vqUlYGSkWj8nB1xF/aqfy8qq/DNU9+ABuOJi\n1fo2Nti9axf2b9gAr9JSLElJwTxXV1zlOAwbORKh77+vXN/WFjA2Vt3//fv8+2/WTGvr//Sf/2D/\nrl3wkssr82JggGETJ2LEe+8p1+fKV6BmNjb8+6/r9/X4cWV7qKqm9pCXV9neqsQDMzP+9lZQoGjP\nSvVNTPjbc2EhUFYGANi9Ywf2//hjeXu5fVs5L+Hh5fWfPlXUV2JkxP/7qmv9oiL++jX9ftVcf/fG\njdi/eXNlTtq1q8zJRx/VP54GyCfHcTC2sgLH9/2q6fuK2rQJu9esgVdJCb5OScEXz9vKiIkTMTI8\n/MXfL9/FecbGLz0+K6mpPRcUKP9eKpiZ8dev/vuqUJvjeVUWFoCRUWVenj3D18nJ5X+L9PUxYvx4\njBw9WqV+veOp+veoqtoeHyrU9PeusLDm7+slx5OqoqKisHvdusq8uLlV/o2WSlX3o8bfi1jqR23Z\ngt0bNsDtf3n4HtngxNzV0sD9jGstMTGRjR8/nkmlUnbt2jX+SgCbA7CjRkZMbmLCmKlp+Xb0KH/9\n4cMZs7Co3Cwty7djx/jrh4QwZm1dudnYlG/Hj/PXHzGCsWbNKrfmzcu3muqHhTHWsqXqduIEf/1R\noxizt1fdTp5kcrmcHdmzh81xdCzPi6MjO9qnD5Pb2zPm4KC8nTrFv/8xYxhzdFTdtLi+3MGBHfny\nS+W87N3L5HI5Y6NHV+akaj5r2n9N39fJk/z1Q0Mr20DVrabv9/33K9tY1XZXU/sJDq5sw1XbdU3t\nedgwxszMGDM1ZXITE3bEyIjNKf8ni3JeKgQGMmZiorrV9Puqa/2goMrfbNVNoPrywEDlnFQcW44c\naZh41J1PNdVXHFuMjSvz0qQJkxsbl9evKT8V7a36Vtvjc8VWU/2QEMasrFS3mtp/1d9X1a22x/OK\n7Xl9lWOusTE7ambG5La2yvVr83uvTTzV/x7V5fhQdXvR8cHcXHV72fGk2iY/ckT1b9HevUweGKgT\nvxe++nJjY3akSRM2+/mxRcxENSI4c+ZMWFtbIzs7GytWrICpqalKnQEchw7m5hgYGUlDzwCO7duH\n4+PGgXN0hDwzk/LyHOWFH+VFFeWEH+WFH+WFH+VF1bF9+/Br8BjcRCGOi6erpUJU5wjeunUL27Zt\nQ3x8PLZv346JEyeq1DkBYGZkJDKTkzUfoAhlJicjIDISTWxsUJKTQ3l5jvLCj/KiinLCj/LCj/LC\nj/KiKjM5Gc6YjXVYIHQoL6buIceLFy8yiUTCGGOsrKyMffzxx+yNN95gEomEpaSkMMYY+/LLL9mI\nESNYeHg4Ky4uZvHx8WzdunW8+9NAyFpp5cqVQocgSpQXfpQXVZQTfpQXfpQXfpQXZadxWqXfUlNf\nSChqHRFcvnw5oqKiYGZmBgCIjo5WrI0VGxuLWbNmITo6GosWLQIAxMfH46OPPgJjDKtXr1ZnaI3O\n48eq91wllJeaUF5UUU74UV74UV74UV5erqa+kFDU2hF0dXXFgQMHMGrUKADAuXPnEBAQAADo3r07\nLl++rFS/S5cu2LZtmzpDIoQQQggRzB9//PHCvpCmqXVB6aCgIBhUucQ6Pz8fFhYWirK+vj7kcrk6\nQ9AZaWlpQocgSpQXfpQXVZQTfpQXfpQXfpSXl8vLyxNVX0ijF4tYWFggPz9fUZbL5XW+mbqLiwu4\nirXTiBIaTeVHeeFHeVFFOeFHeeFHeeFHeVHm4uKiVG6IvlBD0mhH0M/PD4cOHUJwcDAuXryITp06\n1XkfKSkpaoiMEEIIIUT9GqIv1JA00hGsGMELDAzEyZMn4efnBwCIjIzUxNsTQgghhIiC2PpColpQ\nmhBCCCGEaI5wk9Kv4ODBgwgLC1OUL168iB49euDNN99ULEGjqxhjsLe3h7+/P/z9/TFv3jyhQxKM\nXC7HhAkT0LNnT/j7++PWrVtChyQanTt3VrSR8Ir7C+uw2NhY+Pv7Ayg/7eTNN99E7969MWnSJOjq\nv5Gr5uTPP/+Eg4ODos3s2bNH4OiE8ezZM4waNQq9e/dG9+7dcejQIZ1vL3w5+fPPP5X+Dulieykr\nK8O4cePw5ptvolevXkhMTBR/WxFwDcM6mTZtGmvfvj0LDQ1VPObt7c1u377NGGPsnXfeYX/++adQ\n4QkuOTmZvfvuu0KHIQr79+9nY8eOZYyVL2g+ZMgQgSMSh6KiIubj4yN0GKKxbNky5unpyd544w3G\nGGPvvvsuO3PmDGOMsQkTJrCDBw8KGZ4gquckIiKCfffddwJHJbzIyEg2Y8YMxhhjOTk5zNHRkb33\n3ns63V74cvLjjz/qfHuJjo5m4eHhjDHGZDIZe++990TfVrRmRNDPzw8bNmxQ9KTz8vJQXFyMtm3b\nAgAGDBiAU6dOCRmioOLj43Hnzh289dZbGDRoEG7evCl0SIIR2xpNYnH16lUUFhZiwIAB6Nu3L2Jj\nY4UOSVAV65xWHFMSEhLQu3dvAMDAgQN18nhSPSfx8fH49ddf0adPH3z44YcoKCgQOEJhBAcHK2ad\n5HI5DA0Ndb698OWE2gswZMgQbNq0CUD5UjrW1taIj48XdVsRXUdwy5Yt8PT0VNri4+MREhKiVK/6\nOjzm5ubIzc3VdLiC4MtR69atMW/ePPz++++YN28eRo4cKXSYghHbGk1iYWpqis8++wzHjx/Hxo0b\nERYWptN5qb7OKasyXWNmZqYzx5Oqqueke/fuWLFiBc6cOYN27dph4cKFAkYnHFNTU5iZmSE/Px/B\nwcH4+uuvlX47uthequfkm2++Qbdu3ai9oPxvjlQqxfTp0xEWFib6Y4tGl4+pjfDw8Fqdu1R9HZ68\nvDxYWVmpMzTR4MtRUVGR4gDu5+eH7OxsIUITBbGt0SQWr732GlxdXQEAbm5usLW1xd9//w17e3uB\nIxOHqm0kPz9fZ44nLxIYGAhLS0sAwNChQzFt2jSBIxJOZmYmgoKCMHnyZISGhuLzzz9XPKer7aVq\nTkaMGIHc3FxqL89t3boV9+7dQ7du3fD06VPF42JsK1r719HCwgJNmjTB7du3wRjDiRMnFEOvumjR\nokVYtWoVgPIpwDZt2ggckXD8/Pxw5MgRABDFGk1iERkZiVmzZgEAsrOzkZeXh1atWgkclXj4+Pjg\nzJkzAICjR4/q9PGkQkBAAOLi4gAAv/32G7p27SpwRMK4d+8e+vfvj+XLl0MqlQKg9sKXE2ovwI4d\nO7B06VIAgLGxMfT19dG1a1dRtxXRjQi+CMdxSncVqZjeKisrw4ABA+Dr6ytgdMKaM2cORo4ciSNH\njsDAwABbt24VOiTBiG2NJrEIDw/H2LFjFQehyMhIGilF5Tqn3333HT766COUlJTA3d0dw4cPFzgy\n4VTkZOPGjZg8eTIMDQ3RqlUrbN68WeDIhLFkyRLk5uZi0aJFivPiVq9ejWnTpulse+HLyapVqzBj\nxgydbi/Dhw+HVCpFnz598OzZM6xevRrt27cX9bGF1hEkhBBCCNFRNBxACCGEEKKjqCNICCGEEKKj\nqCNICCGEEKKjqCNICCGEEKKjqCNICCGEEKKjqCNICCGEEKKjqCNICGn0vv32W/Tr1w8SiQRvvfUW\n4uPjIZVKMWzYMKV6FQtsb926FW3atIG/vz/8/f3h4+ODKVOmCBE6IYSoFXUECSGNWlJSEg4dOoST\nJ09CJpNh5cqVCA8PB8dxOHfuHKKiolRew3EcRo4cidOnT+P06dNISEjAlStXEB8fL8AnIIQQ9aGO\nICGkUbO0tERGRgb+/e9/486dO/Dy8sKlS5cAAEuXLsWCBQtw584dpdcwxpRuFJ+Xl4fHjx+L7h6h\nhBBSX9QRJIQ0avb29vjll1/wxx9/oGfPnujQoQMOHTqkeG7x4sUIDw9Xed2uXbsgkUjw+uuv4+23\n38YXX3wBFxcXTYdPCCFqRR1BQkijduvWLVhaWmLLli1IT09HVFQUJkyYgJycHHAchw8++ADm5ubY\nsGGD0uvCwsIgk8lw/Phx5Ofnw83NTaBPQAgh6kMdQUJIo/bXX39h8uTJePbsGQDAzc0N1tbW0NfX\nV0z/btiwAStWrEB+fr7idRXPOTs7Y/369QgODkZRUZHmPwAhhKgRdQQJIY1aYGAgevXqBV9fX7z5\n5psICAjAihUrYGlpCY7jAADNmjXDypUrFR09juMUzwFA37598fbbb+Orr74S4iMQQojacKzqGdGE\nEEIIIURn0IggIYQQQoiOoo4gIYQQQoiOoo4gIYQQQoiOoo4gIYQQQoiOoo4gIYQQQoiOoo4gIYQQ\nQoiOoo4gIYQQQoiOoo4gIYQQQoiO+n+cIhN1BXtRSQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4628490>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plot the Capacity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_capacity(max_iterations, ax=None):\n",
      "    # xxxxx Plot Sum Capacity (all) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "    sum_capacity_alt_min = alt_min_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_alt_min = alt_min_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_alt_min = np.abs([i[1] - i[0] for i in sum_capacity_CF_alt_min])\n",
      "\n",
      "    sum_capacity_closed_form = closed_form_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_closed_form = closed_form_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_closed_form = np.abs([i[1] - i[0] for i in sum_capacity_CF_closed_form])\n",
      "\n",
      "    # sum_capacity_max_sinr = max_sinrn_results.get_result_values_list(\n",
      "    #     'sum_capacity',\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # sum_capacity_CF_max_sinr = max_sinrn_results.get_result_values_confidence_intervals(\n",
      "    #     'sum_capacity',\n",
      "    #     P=95,\n",
      "    #     fixed_params={'max_iterations': max_iterations})\n",
      "    # sum_capacity_errors_max_sinr = np.abs([i[1] - i[0] for i in sum_capacity_CF_max_sinr])\n",
      "\n",
      "    # sum_capacity_min_leakage = min_leakage_results.get_result_values_list('sum_capacity')\n",
      "    # sum_capacity_CF_min_leakage = min_leakage_results.get_result_values_confidence_intervals('sum_capacity', P=95)\n",
      "    # sum_capacity_errors_min_leakage = np.abs([i[1] - i[0] for i in sum_capacity_CF_min_leakage])\n",
      "\n",
      "    sum_capacity_mmse = mmse_results.get_result_values_list(\n",
      "        'sum_capacity',\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_CF_mmse = mmse_results.get_result_values_confidence_intervals(\n",
      "        'sum_capacity',\n",
      "        P=95,\n",
      "        fixed_params={'max_iterations': max_iterations})\n",
      "    sum_capacity_errors_mmse = np.abs([i[1] - i[0] for i in sum_capacity_CF_mmse])\n",
      "\n",
      "    if ax is None:\n",
      "        fig, ax = plt.subplots(nrows=1, ncols=1)\n",
      "    # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "    ax.errorbar(SNR_alt_min, sum_capacity_alt_min, sum_capacity_errors_alt_min, fmt='-r*', elinewidth=2.0, label='Alt. Min.')\n",
      "    ax.errorbar(SNR_closed_form, sum_capacity_closed_form, sum_capacity_errors_closed_form, fmt='-b*', elinewidth=2.0, label='Closed Form')\n",
      "    # ax.errorbar(SNR_max_SINR, sum_capacity_max_sinr, sum_capacity_errors_max_sinr, fmt='-g*', elinewidth=2.0, label='Max SINR')\n",
      "    # ax.errorbar(SNR, sum_capacity_min_leakage, sum_capacity_errors_min_leakage, fmt='-k*', elinewidth=2.0, label='Min Leakage.')\n",
      "    ax.errorbar(SNR_mmse, sum_capacity_mmse, sum_capacity_errors_mmse, fmt='-m*', elinewidth=2.0, label='MMSE.')\n",
      "    # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "    ax.set_xlabel('SNR')\n",
      "    ax.set_ylabel('Sum Capacity')\n",
      "    title = 'Sum Capacity for Different Algorithms ({max_iterations} Max Iterations)\\nK={K}, Nr={Nr}, Nt={Nt}, Ns={Ns}, {M}-{modulator}'.replace(\"{max_iterations}\", str(max_iterations))\n",
      "    ax.set_title(title.format(**alt_min_results.params.parameters))\n",
      "\n",
      "    #leg = ax.legend(fancybox=True, shadow=True, loc=2)\n",
      "    leg = ax.legend(fancybox=True, shadow=True, loc='lower right', bbox_to_anchor=(0.99, 0.01), ncol=4)\n",
      "    \n",
      "    ax.grid(True, which='both', axis='both')\n",
      "    \n",
      "    # Lets plot the mean number of ia iterations\n",
      "    ax2 = ax.twinx()\n",
      "    mean_alt_min_ia_terations = get_num_mean_ia_iterations(alt_min_results, {'max_iterations': max_iterations})\n",
      "    # mean_max_sinrn_ia_terations = get_num_mean_ia_iterations(max_sinrn_results, {'max_iterations': max_iterations})\n",
      "    mean_mmse_ia_terations = get_num_mean_ia_iterations(mmse_results, {'max_iterations': max_iterations})\n",
      "    ax2.plot(SNR_alt_min, mean_alt_min_ia_terations, '--r*')\n",
      "    # ax2.plot(SNR_max_SINR, mean_max_sinrn_ia_terations, '--g*')\n",
      "    ax2.plot(SNR_mmse, mean_mmse_ia_terations, '--m*')\n",
      "    \n",
      "    # Horizontal line with the max alowed ia iterations\n",
      "    ax2.hlines(max_iterations, SNR_alt_min[0], SNR_alt_min[-1], linestyles='dashed')\n",
      "    ax2.set_ylim(0, max_iterations*1.1)\n",
      "    ax2.set_ylabel('IA Mean Iterations')\n",
      "\n",
      "    # Set the X axis limits\n",
      "    ax.set_xlim(SNR_alt_min[0], SNR_alt_min[-1])\n",
      "    # Set the Y axis limits\n",
      "    #ax.set_ylim(1e-6, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(1,1,figsize=(10,7.5))\n",
      "plot_capacity(120, ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nq8qWLYu7d+8aoiwiIiKibFWrVi2cPXvW2GWkySCHbhs3bqw7C/vdu3cRGxuL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\ne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       "text": [
        "<matplotlib.figure.Figure at 0x79c6310>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}